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Pacal I, Ozdemir B, Zeynalov J, Gasimov H, Pacal N. A novel CNN-ViT-based deep learning model for early skin cancer diagnosis. Biomed Signal Process Control 2025; 104:107627. [DOI: 10.1016/j.bspc.2025.107627] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2025]
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2
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Sun X, Ma J, Li Y. Efficient polyp detection algorithm based on deep learning. Scand J Gastroenterol 2025; 60:502-515. [PMID: 40358097 DOI: 10.1080/00365521.2025.2503297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2025] [Revised: 04/23/2025] [Accepted: 05/01/2025] [Indexed: 05/15/2025]
Abstract
OBJECTIVE Colon polyp detection is crucial in reducing the incidence of colorectal cancer. However, due to the diverse morphology of colon polyps, their high similarity to surrounding tissues, and the difficulty of detecting small target polyps, false negatives and false positives are common problems. METHODS To address this, we propose a lightweight and efficient colon polyp detection model based on YOLOv10, a deep learning-based object detection method-EP-YOLO (Efficient for Polyp). By introducing the GBottleneck module, we reduce the number of parameters and accelerate inference; a lightweight GHead detection head and an additional small target detection layer are designed to enhance small target recognition ability; we propose the SE_SPPF module to improve attention on polyps while suppressing background noise interference; the loss function is replaced with Wise-IoU to optimize gradient distribution and improve generalization ability. RESULTS Experimental results on the publicly available LDPolypVideo (7,681 images), Kvasir-SEG (1,000 images) and CVC-ClinicDB (612 images) datasets show that EP-YOLO achieves precision scores of 94.17%, 94.32% and 93.21%, respectively, representing improvements of 2.10%, 2.05% and 1.42% over the baseline algorithm, while reducing the number of parameters by 16%. CONCLUSION Compared with other mainstream object detection methods, EP-YOLO demonstrates significant advantages in accuracy, computational load and FPS, making it more suitable for practical medical scenarios in colon polyp detection.
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Affiliation(s)
- Xing Sun
- College of Medical Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Jingang Ma
- College of Medical Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yang Li
- College of Medical Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan, China
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3
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Sasmal P, Kumar Panigrahi S, Panda SL, Bhuyan MK. Attention-guided deep framework for polyp localization and subsequent classification via polyp local and Siamese feature fusion. Med Biol Eng Comput 2025:10.1007/s11517-025-03369-z. [PMID: 40314710 DOI: 10.1007/s11517-025-03369-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2024] [Accepted: 04/16/2025] [Indexed: 05/03/2025]
Abstract
Colorectal cancer (CRC) is one of the leading causes of death worldwide. This paper proposes an automated diagnostic technique to detect, localize, and classify polyps in colonoscopy video frames. The proposed model adopts the deep YOLOv4 model that incorporates both spatial and contextual information in the form of spatial attention and channel attention blocks, respectively for better localization of polyps. Finally, leveraging a fusion of deep and handcrafted features, the detected polyps are classified as adenoma or non-adenoma. Polyp shape and texture are essential features in discriminating polyp types. Therefore, the proposed work utilizes a pyramid histogram of oriented gradient (PHOG) and embedding features learned via triplet Siamese architecture to extract these features. The PHOG extracts local shape information from each polyp class, whereas the Siamese network extracts intra-polyp discriminating features. The individual and cross-database performances on two databases suggest the robustness of our method in polyp localization. The competitive analysis based on significant clinical parameters with current state-of-the-art methods confirms that our method can be used for automated polyp localization in both real-time and offline colonoscopic video frames. Our method provides an average precision of 0.8971 and 0.9171 and an F1 score of 0.8869 and 0.8812 for the Kvasir-SEG and SUN databases. Similarly, the proposed classification framework for the detected polyps yields a classification accuracy of 96.66% on a publicly available UCI colonoscopy video dataset. Moreover, the classification framework provides an F1 score of 96.54% that validates the potential of the proposed framework in polyp localization and classification.
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Affiliation(s)
- Pradipta Sasmal
- Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, 721302, India.
| | - Susant Kumar Panigrahi
- Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, 721302, India
| | - Swarna Laxmi Panda
- Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela, Odisha, 769008, India
| | - M K Bhuyan
- Department of Electronics and Electrical Engineering, Indian Institute of Technology, Guwahati, Assam, 781039, India
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4
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Ozdemir B, Pacal I. A robust deep learning framework for multiclass skin cancer classification. Sci Rep 2025; 15:4938. [PMID: 39930026 PMCID: PMC11811178 DOI: 10.1038/s41598-025-89230-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2024] [Accepted: 02/04/2025] [Indexed: 02/13/2025] Open
Abstract
Skin cancer represents a significant global health concern, where early and precise diagnosis plays a pivotal role in improving treatment efficacy and patient survival rates. Nonetheless, the inherent visual similarities between benign and malignant lesions pose substantial challenges to accurate classification. To overcome these obstacles, this study proposes an innovative hybrid deep learning model that combines ConvNeXtV2 blocks and separable self-attention mechanisms, tailored to enhance feature extraction and optimize classification performance. The inclusion of ConvNeXtV2 blocks in the initial two stages is driven by their ability to effectively capture fine-grained local features and subtle patterns, which are critical for distinguishing between visually similar lesion types. Meanwhile, the adoption of separable self-attention in the later stages allows the model to selectively prioritize diagnostically relevant regions while minimizing computational complexity, addressing the inefficiencies often associated with traditional self-attention mechanisms. The model was comprehensively trained and validated on the ISIC 2019 dataset, which includes eight distinct skin lesion categories. Advanced methodologies such as data augmentation and transfer learning were employed to further enhance model robustness and reliability. The proposed architecture achieved exceptional performance metrics, with 93.48% accuracy, 93.24% precision, 90.70% recall, and a 91.82% F1-score, outperforming over ten Convolutional Neural Network (CNN) based and over ten Vision Transformer (ViT) based models tested under comparable conditions. Despite its robust performance, the model maintains a compact design with only 21.92 million parameters, making it highly efficient and suitable for model deployment. The Proposed Model demonstrates exceptional accuracy and generalizability across diverse skin lesion classes, establishing a reliable framework for early and accurate skin cancer diagnosis in clinical practice.
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Affiliation(s)
- Burhanettin Ozdemir
- Department of Operations and Project Management, College of Business, Alfaisal University, Riyadh, 11533, Saudi Arabia.
| | - Ishak Pacal
- Department of Computer Engineering, Faculty of Engineering, Igdir University, Igdir, 76000, Turkey
- Department of Electronics and Information Technologies, Faculty of Architecture and Engineering, Nakhchivan State University, AZ 7012, Nakhchivan, Azerbaijan
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5
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Han T, Wang H, Li T, Liu Q, Huang Y. MHO: A Modified Hippopotamus Optimization Algorithm for Global Optimization and Engineering Design Problems. Biomimetics (Basel) 2025; 10:90. [PMID: 39997113 PMCID: PMC11852793 DOI: 10.3390/biomimetics10020090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2025] [Revised: 02/03/2025] [Accepted: 02/03/2025] [Indexed: 02/26/2025] Open
Abstract
The hippopotamus optimization algorithm (HO) is a novel metaheuristic algorithm that solves optimization problems by simulating the behavior of hippopotamuses. However, the traditional HO algorithm may encounter performance degradation and fall into local optima when dealing with complex global optimization and engineering design problems. In order to solve these problems, this paper proposes a modified hippopotamus optimization algorithm (MHO) to enhance the convergence speed and solution accuracy of the HO algorithm by introducing a sine chaotic map to initialize the population, changing the convergence factor in the growth mechanism, and incorporating the small-hole imaging reverse learning strategy. The MHO algorithm is tested on 23 benchmark functions and successfully solves three engineering design problems. According to the experimental data, the MHO algorithm obtains optimal performance on 13 of these functions and three design problems, exits the local optimum faster, and has better ordering and stability than the other nine metaheuristics. This study proposes the MHO algorithm, which offers fresh insights into practical engineering problems and parameter optimization.
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Affiliation(s)
| | | | - Tingting Li
- School of Electrical & Information Engineering, Anhui University of Science and Technology, Huainan 232001, China; (T.H.); (H.W.); (Q.L.); (Y.H.)
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6
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Alotaibi SR, Alohali MA, Maashi M, Alqahtani H, Alotaibi M, Mahmud A. Advances in colorectal cancer diagnosis using optimal deep feature fusion approach on biomedical images. Sci Rep 2025; 15:4200. [PMID: 39905104 PMCID: PMC11794880 DOI: 10.1038/s41598-024-83466-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Accepted: 12/16/2024] [Indexed: 02/06/2025] Open
Abstract
Colorectal cancer (CRC) is the second popular cancer in females and third in males, with an increased number of cases. Pathology diagnoses complemented with predictive and prognostic biomarker information is the first step for personalized treatment. Histopathological image (HI) analysis is the benchmark for pathologists to rank colorectal cancer of various kinds. However, pathologists' diagnoses are highly subjective and susceptible to inaccurate diagnoses. The improved diagnosis load in the pathology laboratory, incorporated with the reported intra- and inter-variability in the biomarker assessment, has prompted the quest for consistent machine-based techniques to be integrated into routine practice. In the healthcare field, artificial intelligence (AI) has achieved extraordinary achievements in healthcare applications. Lately, computer-aided diagnosis (CAD) based on HI has progressed rapidly with the increase of machine learning (ML) and deep learning (DL) based models. This study introduces a novel Colorectal Cancer Diagnosis using the Optimal Deep Feature Fusion Approach on Biomedical Images (CCD-ODFFBI) method. The primary objective of the CCD-ODFFBI technique is to examine the biomedical images to identify colorectal cancer (CRC). In the CCD-ODFFBI technique, the median filtering (MF) approach is initially utilized for noise elimination. The CCD-ODFFBI technique utilizes a fusion of three DL models, MobileNet, SqueezeNet, and SE-ResNet, for feature extraction. Moreover, the DL models' hyperparameter selection is performed using the Osprey optimization algorithm (OOA). Finally, the deep belief network (DBN) model is employed to classify CRC. A series of simulations is accomplished to highlight the significant results of the CCD-ODFFBI method under the Warwick-QU dataset. The comparison of the CCD-ODFFBI method showed a superior accuracy value of 99.39% over existing techniques.
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Affiliation(s)
- Sultan Refa Alotaibi
- Department of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Shaqra, Saudi Arabia
| | - Manal Abdullah Alohali
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
| | - Mashael Maashi
- Department of Software Engineering, College of Computer and Information Sciences, King Saud University, PO Box 103786, Riyadh, 11543, Saudi Arabia
| | - Hamed Alqahtani
- Department of Information Systems, College of Computer Science, Center of Artificial Intelligence, Unit of Cybersecurity, King Khalid University, Abha, Saudi Arabia
| | - Moneerah Alotaibi
- Department of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Shaqra, Saudi Arabia
| | - Ahmed Mahmud
- Research Center, Future University in Egypt, New Cairo, 11835, Egypt
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7
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Wang KN, Wang H, Zhou GQ, Wang Y, Yang L, Chen Y, Li S. TSdetector: Temporal-Spatial self-correction collaborative learning for colonoscopy video detection. Med Image Anal 2025; 100:103384. [PMID: 39579624 DOI: 10.1016/j.media.2024.103384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 09/24/2024] [Accepted: 10/28/2024] [Indexed: 11/25/2024]
Abstract
CNN-based object detection models that strike a balance between performance and speed have been gradually used in polyp detection tasks. Nevertheless, accurately locating polyps within complex colonoscopy video scenes remains challenging since existing methods ignore two key issues: intra-sequence distribution heterogeneity and precision-confidence discrepancy. To address these challenges, we propose a novel Temporal-Spatial self-correction detector (TSdetector), which first integrates temporal-level consistency learning and spatial-level reliability learning to detect objects continuously. Technically, we first propose a global temporal-aware convolution, assembling the preceding information to dynamically guide the current convolution kernel to focus on global features between sequences. In addition, we designed a hierarchical queue integration mechanism to combine multi-temporal features through a progressive accumulation manner, fully leveraging contextual consistency information together with retaining long-sequence-dependency features. Meanwhile, at the spatial level, we advance a position-aware clustering to explore the spatial relationships among candidate boxes for recalibrating prediction confidence adaptively, thus eliminating redundant bounding boxes efficiently. The experimental results on three publicly available polyp video dataset show that TSdetector achieves the highest polyp detection rate and outperforms other state-of-the-art methods. The code can be available at https://github.com/soleilssss/TSdetector.
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Affiliation(s)
- Kai-Ni Wang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China; Jiangsu Key Laboratory of Biomaterials and Devices, Southeast University, Nanjing, China
| | - Haolin Wang
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China; Jiangsu Key Laboratory of Biomaterials and Devices, Southeast University, Nanjing, China
| | - Guang-Quan Zhou
- School of Biological Science and Medical Engineering, Southeast University, Nanjing, China; Jiangsu Key Laboratory of Biomaterials and Devices, Southeast University, Nanjing, China.
| | | | - Ling Yang
- Institute of Medical Technology, Peking University Health Science Center, China
| | - Yang Chen
- Laboratory of Image Science and Technology, Southeast University, Nanjing, China; Key Laboratory of Computer Network and Information Integration, Southeast University, Nanjing, China
| | - Shuo Li
- Department of Computer and Data Science and Department of Biomedical Engineering, Case Western Reserve University, USA
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8
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Raju ASN, Venkatesh K, Rajababu M, Gatla RK, Eid MM, Ali E, Titova N, Sharaf ABA. A hybrid framework for colorectal cancer detection and U-Net segmentation using polynetDWTCADx. Sci Rep 2025; 15:847. [PMID: 39757273 PMCID: PMC11701104 DOI: 10.1038/s41598-025-85156-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2024] [Accepted: 01/01/2025] [Indexed: 01/07/2025] Open
Abstract
"PolynetDWTCADx" is a sophisticated hybrid model that was developed to identify and distinguish colorectal cancer. In this study, the CKHK-22 dataset, comprising 24 classes, served as the introduction. The proposed method, which combines CNNs, DWTs, and SVMs, enhances the accuracy of feature extraction and classification. The study employs DWT to optimize and enhance two integrated CNN models before classifying them with SVM following a systematic procedure. PolynetDWTCADx was the most effective model that we evaluated. It was capable of attaining a moderate level of recall, as well as an area under the curve (AUC) and accuracy during testing. The testing accuracy was 92.3%, and the training accuracy was 95.0%. This demonstrates that the model is capable of distinguishing between noncancerous and cancerous lesions in the colon. We can also employ the semantic segmentation algorithms of the U-Net architecture to accurately identify and segment cancerous colorectal regions. We assessed the model's exceptional success in segmenting and providing precise delineation of malignant tissues using its maximal IoU value of 0.93, based on intersection over union (IoU) scores. When these techniques are added to PolynetDWTCADx, they give doctors detailed visual information that is needed for diagnosis and planning treatment. These techniques are also very good at finding and separating colorectal cancer. PolynetDWTCADx has the potential to enhance the recognition and management of colorectal cancer, as this study underscores.
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Affiliation(s)
- Akella S Narasimha Raju
- Department of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering, Dundigal, Hyderabad, 500043, Telangana, India.
| | - K Venkatesh
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, Tamilnadu, India.
| | - Makineedi Rajababu
- Department of Information Technology, Aditya University, Surampalem, 533437, Andhra Pradesh, India
| | - Ranjith Kumar Gatla
- Department of Computer Science and Engineering (Data Science), Institute of Aeronautical Engineering, Dundigal, Hyderabad, 500043, Telangana, India
| | - Marwa M Eid
- Department of physical therapy, College of Applied Medical Science, Taif University, Taif, 21944, Saudi Arabia
| | - Enas Ali
- University Centre for Research and Development, Chandigarh University, Mohali, 140413, Punjab, India
| | - Nataliia Titova
- Biomedical Engineering Department, National University Odesa Polytechnic, Odesa, 65044, Ukraine.
| | - Ahmed B Abou Sharaf
- Ministry of Higher Education & Scientific Research, Industrial Technical Institute in Mataria, Cairo, 11718, Egypt
- Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh, 174103, India
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9
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Wan J, Zhu W, Chen B, Wang L, Chang K, Meng X. CRH-YOLO for precise and efficient detection of gastrointestinal polyps. Sci Rep 2024; 14:30033. [PMID: 39627309 PMCID: PMC11615362 DOI: 10.1038/s41598-024-81842-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2024] [Accepted: 11/29/2024] [Indexed: 12/06/2024] Open
Abstract
Gastrointestinal polyps are early indicators of many significant diseases within the digestive system, and timely detection of these polyps is crucial for preventing them. Although clinical gastrointestinal endoscopy and interventions help reduce the risk of malignancy, most current methods fail to adequately address the uncertainties and scale issues associated with the presence of polyps, posing a threat to patients' health. Therefore, this paper proposes a novel single-stage method for polyp detection. Specifically, by designing the CRFEM, the network's ability to perceive contextual information about polyp targets is enhanced. Additionally, the RSPPF is designed to assist the network in more meticulously completing the fusion of multi-scale polyp features. Finally, one detection head is removed from the original model to reduce a substantial number of parameters, and a high-dimensional feature compensation structure is designed to address the decline in recall rate caused by the absence of the detection head. Experiments were conducted using public datasets such as Kvasir-seg, which includes gastric and intestinal polyps. The results indicate that CRH-YOLO achieves 88.8%, 86.0%, and 90.7% on three key metrics: Precision (P), Recall (R), and mean average precision at 0.5 (map@.5), significantly outperforming current mainstream detection models like YOLOv8n. Notably, CRH-YOLO improves the map@.5 metric by 2.4% compared to YOLOv8n. Furthermore, the model demonstrates excellent performance in detecting smaller or less obvious polyps, providing an effective solution for the early detection and prediction of polyps.
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Affiliation(s)
- Jingjing Wan
- Department of Gastroenterology, The Second People's Hospital of Huai'an, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huaian, 223002, China.
| | - Wenjie Zhu
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China
| | - Bolun Chen
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China
| | - Ling Wang
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China.
| | - Kailu Chang
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China
| | - Xianchun Meng
- Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, 223003, China
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Pacal I, Celik O, Bayram B, Cunha A. Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification. CLUSTER COMPUTING 2024; 27:11187-11212. [DOI: 10.1007/s10586-024-04532-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 04/08/2024] [Accepted: 04/22/2024] [Indexed: 05/14/2025]
Abstract
AbstractThe early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model’s ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model’s decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes.
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Yaqoob A, Verma NK, Aziz RM, Shah MA. RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques. J Cancer Res Clin Oncol 2024; 150:455. [PMID: 39390265 PMCID: PMC11467072 DOI: 10.1007/s00432-024-05968-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2024] [Accepted: 09/21/2024] [Indexed: 10/12/2024]
Abstract
PROBLEM Breast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process. AIM This study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data's high dimensionality and complexity. METHODS We introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model's performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India. RESULTS The proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls. CONCLUSION Our findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.
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Affiliation(s)
- Abrar Yaqoob
- School of Advanced Science and Language, VIT Bhopal University, Kothrikalan, Sehore, Bhopal, 466114, India.
| | - Navneet Kumar Verma
- School of Advanced Science and Language, VIT Bhopal University, Kothrikalan, Sehore, Bhopal, 466114, India
| | - Rabia Musheer Aziz
- Planning Department, State Planning Institute (New Division), Lucknow, Utter Pradesh, 226001, India
| | - Mohd Asif Shah
- Department of Economics, Kardan University, Parwane Du, 1001, Kabul, Afghanistan.
- Division of Research and Development, Lovely Professional University, Phagwara, Punjab, 144001, India.
- Centre of Research Impact and Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
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12
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Lubbad MAH, Kurtulus IL, Karaboga D, Kilic K, Basturk A, Akay B, Nalbantoglu OU, Yilmaz OMD, Ayata M, Yilmaz S, Pacal I. A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:2559-2580. [PMID: 38565730 PMCID: PMC11522249 DOI: 10.1007/s10278-024-01086-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 02/28/2024] [Accepted: 03/12/2024] [Indexed: 04/04/2024]
Abstract
This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system's potential in clinical practices and to offer a strategic framework for improving diagnosis and treatment processes in implantology. This study employed a total of 28 different deep learning models, including 18 convolutional neural network (CNN) models (VGG, ResNet, DenseNet, EfficientNet, RegNet, ConvNeXt) and 10 vision transformer models (Swin and Vision Transformer). The dataset comprises 1258 panoramic radiographs from patients who received implant treatments at Erciyes University Faculty of Dentistry between 2012 and 2023. It is utilized for the training and evaluation process of deep learning models and consists of prototypes from six different implant systems provided by six manufacturers. The deep learning-based dental implant system provided high classification accuracy for different dental implant brands using deep learning models. Furthermore, among all the architectures evaluated, the small model of the ConvNeXt architecture achieved an impressive accuracy rate of 94.2%, demonstrating a high level of classification success.This study emphasizes the effectiveness of deep learning-based systems in achieving high classification accuracy in dental implant types. These findings pave the way for integrating advanced deep learning tools into clinical practice, promising significant improvements in patient care and treatment outcomes.
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Affiliation(s)
- Mohammed A H Lubbad
- Department of Computer Engineering, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey.
- Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey.
| | | | - Dervis Karaboga
- Department of Computer Engineering, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey
- Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey
| | - Kerem Kilic
- Department of Prosthodontics, Dentistry Faculty, Erciyes University, Kayseri, Turkey
| | - Alper Basturk
- Department of Computer Engineering, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey
- Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey
| | - Bahriye Akay
- Department of Computer Engineering, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey
- Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey
| | - Ozkan Ufuk Nalbantoglu
- Department of Computer Engineering, Engineering Faculty, Erciyes University, 38039, Kayseri, Turkey
- Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey
| | | | - Mustafa Ayata
- Department of Prosthodontics, Dentistry Faculty, Erciyes University, Kayseri, Turkey
| | - Serkan Yilmaz
- Department of Dentomaxillofacial Radiology, Dentistry Faculty, Erciyes University, Kayseri, Turkey
| | - Ishak Pacal
- Department of Computer Engineering, Engineering Faculty, Igdir University, Igdir, Turkey
- Artificial Intelligence and Big Data Application and Research Center, Erciyes University, Kayseri, Turkey
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Alotaibi M, Alshardan A, Maashi M, Asiri MM, Alotaibi SR, Yafoz A, Alsini R, Khadidos AO. Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model. Sci Rep 2024; 14:20434. [PMID: 39227664 PMCID: PMC11372073 DOI: 10.1038/s41598-024-71302-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Accepted: 08/27/2024] [Indexed: 09/05/2024] Open
Abstract
Cancer seems to have a vast number of deaths due to its heterogeneity, aggressiveness, and significant propensity for metastasis. The predominant categories of cancer that may affect males and females and occur worldwide are colon and lung cancer. A precise and on-time analysis of this cancer can increase the survival rate and improve the appropriate treatment characteristics. An efficient and effective method for the speedy and accurate recognition of tumours in the colon and lung areas is provided as an alternative to cancer recognition methods. Earlier diagnosis of the disease on the front drastically reduces the chance of death. Machine learning (ML) and deep learning (DL) approaches can accelerate this cancer diagnosis, facilitating researcher workers to study a vast majority of patients in a limited period and at a low cost. This research presents Histopathological Imaging for the Early Detection of Lung and Colon Cancer via Ensemble DL (HIELCC-EDL) model. The HIELCC-EDL technique utilizes histopathological images to identify lung and colon cancer (LCC). To achieve this, the HIELCC-EDL technique uses the Wiener filtering (WF) method for noise elimination. In addition, the HIELCC-EDL model uses the channel attention Residual Network (CA-ResNet50) model for learning complex feature patterns. Moreover, the hyperparameter selection of the CA-ResNet50 model is performed using the tuna swarm optimization (TSO) technique. Finally, the detection of LCC is achieved by using the ensemble of three classifiers such as extreme learning machine (ELM), competitive neural networks (CNNs), and long short-term memory (LSTM). To illustrate the promising performance of the HIELCC-EDL model, a complete set of experimentations was performed on a benchmark dataset. The experimental validation of the HIELCC-EDL model portrayed a superior accuracy value of 99.60% over recent approaches.
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Affiliation(s)
- Moneerah Alotaibi
- Department of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Shaqra, Saudi Arabia
| | - Amal Alshardan
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia
| | - Mashael Maashi
- Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, 11543, Riyadh, Saudi Arabia
| | - Mashael M Asiri
- Department of Computer Science, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia.
| | - Sultan Refa Alotaibi
- Department of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Shaqra, Saudi Arabia
| | - Ayman Yafoz
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Raed Alsini
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Alaa O Khadidos
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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14
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Pacal I. A novel Swin transformer approach utilizing residual multi-layer perceptron for diagnosing brain tumors in MRI images. INT J MACH LEARN CYB 2024; 15:3579-3597. [DOI: 10.1007/s13042-024-02110-w] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 01/24/2024] [Indexed: 05/14/2025]
Abstract
AbstractSerious consequences due to brain tumors necessitate a timely and accurate diagnosis. However, obstacles such as suboptimal imaging quality, issues with data integrity, varying tumor types and stages, and potential errors in interpretation hinder the achievement of precise and prompt diagnoses. The rapid identification of brain tumors plays a pivotal role in ensuring patient safety. Deep learning-based systems hold promise in aiding radiologists to make diagnoses swiftly and accurately. In this study, we present an advanced deep learning approach based on the Swin Transformer. The proposed method introduces a novel Hybrid Shifted Windows Multi-Head Self-Attention module (HSW-MSA) along with a rescaled model. This enhancement aims to improve classification accuracy, reduce memory usage, and simplify training complexity. The Residual-based MLP (ResMLP) replaces the traditional MLP in the Swin Transformer, thereby improving accuracy, training speed, and parameter efficiency. We evaluate the Proposed-Swin model on a publicly available brain MRI dataset with four classes, using only test data. Model performance is enhanced through the application of transfer learning and data augmentation techniques for efficient and robust training. The Proposed-Swin model achieves a remarkable accuracy of 99.92%, surpassing previous research and deep learning models. This underscores the effectiveness of the Swin Transformer with HSW-MSA and ResMLP improvements in brain tumor diagnosis. This method introduces an innovative diagnostic approach using HSW-MSA and ResMLP in the Swin Transformer, offering potential support to radiologists in timely and accurate brain tumor diagnosis, ultimately improving patient outcomes and reducing risks.
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15
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Chen J, Wang G, Zhou J, Zhang Z, Ding Y, Xia K, Xu X. AI support for colonoscopy quality control using CNN and transformer architectures. BMC Gastroenterol 2024; 24:257. [PMID: 39123140 PMCID: PMC11316311 DOI: 10.1186/s12876-024-03354-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 08/06/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Construct deep learning models for colonoscopy quality control using different architectures and explore their decision-making mechanisms. METHODS A total of 4,189 colonoscopy images were collected from two medical centers, covering different levels of bowel cleanliness, the presence of polyps, and the cecum. Using these data, eight pre-trained models based on CNN and Transformer architectures underwent transfer learning and fine-tuning. The models' performance was evaluated using metrics such as AUC, Precision, and F1 score. Perceptual hash functions were employed to detect image changes, enabling real-time monitoring of colonoscopy withdrawal speed. Model interpretability was analyzed using techniques such as Grad-CAM and SHAP. Finally, the best-performing model was converted to ONNX format and deployed on device terminals. RESULTS The EfficientNetB2 model outperformed other architectures on the validation set, achieving an accuracy of 0.992. It surpassed models based on other CNN and Transformer architectures. The model's precision, recall, and F1 score were 0.991, 0.989, and 0.990, respectively. On the test set, the EfficientNetB2 model achieved an average AUC of 0.996, with a precision of 0.948 and a recall of 0.952. Interpretability analysis showed the specific image regions the model used for decision-making. The model was converted to ONNX format and deployed on device terminals, achieving an average inference speed of over 60 frames per second. CONCLUSIONS The AI-assisted quality system, based on the EfficientNetB2 model, integrates four key quality control indicators for colonoscopy. This integration enables medical institutions to comprehensively manage and enhance these indicators using a single model, showcasing promising potential for clinical applications.
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Affiliation(s)
- Jian Chen
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Suzhou, 215500, China
| | - Ganhong Wang
- Department of Gastroenterology, Changshu Traditional Chinese Medicine Hospital (New District Hospital), Suzhou, 215500, China
| | - Jingjie Zhou
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Suzhou, 215500, China
| | - Zihao Zhang
- Shanghai Haoxiong Education Technology Co., Ltd, Shanghai, 200434, China
| | - Yu Ding
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Suzhou, 215500, China
| | - Kaijian Xia
- Department of Information Engineering, Changshu Hospital Affiliated to Soochow University, Suzhou, 215500, China.
| | - Xiaodan Xu
- Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Suzhou, 215500, China.
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16
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Jiang Y, Zhang Z, Hu Y, Li G, Wan X, Wu S, Cui S, Huang S, Li Z. ECC-PolypDet: Enhanced CenterNet With Contrastive Learning for Automatic Polyp Detection. IEEE J Biomed Health Inform 2024; 28:4785-4796. [PMID: 37983159 DOI: 10.1109/jbhi.2023.3334240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Accurate polyp detection is critical for early colorectal cancer diagnosis. Although remarkable progress has been achieved in recent years, the complex colon environment and concealed polyps with unclear boundaries still pose severe challenges in this area. Existing methods either involve computationally expensive context aggregation or lack prior modeling of polyps, resulting in poor performance in challenging cases. In this paper, we propose the Enhanced CenterNet with Contrastive Learning (ECC-PolypDet), a two-stage training & end-to-end inference framework that leverages images and bounding box annotations to train a general model and fine-tune it based on the inference score to obtain a final robust model. Specifically, we conduct Box-assisted Contrastive Learning (BCL) during training to minimize the intra-class difference and maximize the inter-class difference between foreground polyps and backgrounds, enabling our model to capture concealed polyps. Moreover, to enhance the recognition of small polyps, we design the Semantic Flow-guided Feature Pyramid Network (SFFPN) to aggregate multi-scale features and the Heatmap Propagation (HP) module to boost the model's attention on polyp targets. In the fine-tuning stage, we introduce the IoU-guided Sample Re-weighting (ISR) mechanism to prioritize hard samples by adaptively adjusting the loss weight for each sample during fine-tuning. Extensive experiments on six large-scale colonoscopy datasets demonstrate the superiority of our model compared with previous state-of-the-art detectors.
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17
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Kunduracioglu I, Pacal I. Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseases. JOURNAL OF PLANT DISEASES AND PROTECTION 2024; 131:1061-1080. [DOI: 10.1007/s41348-024-00896-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 02/20/2024] [Indexed: 05/14/2025]
Abstract
AbstractPlant diseases cause significant agricultural losses, demanding accurate detection methods. Traditional approaches relying on expert knowledge may be biased, but advancements in computing, particularly deep learning, offer non-experts effective tools. This study focuses on fine-tuning cutting-edge pre-trained CNN and vision transformer models to classify grape leaves and diagnose grape leaf diseases through digital images. Our research examined a PlantVillage dataset, which comprises 4062 leaf images distributed across four categories. Additionally, we utilized the Grapevine dataset, consisting of 500 leaf images. This dataset is organized into five distinct groups, with each group containing 100 images corresponding to one of the five grape types. The PlantVillage dataset focuses on four classes related to grape diseases, namely Black Rot, Leaf Blight, Healthy, and Esca leaves. On the other hand, the Grapevine dataset includes five classes for leaf recognition, specifically Ak, Alaidris, Buzgulu, Dimnit, and Nazli. In experiments with 14 CNN and 17 vision transformer models, deep learning demonstrated high accuracy in distinguishing grape diseases and recognizing leaves. Notably, four models achieved 100% accuracy on PlantVillage and Grapevine datasets, with Swinv2-Base standing out. This approach holds promise for enhancing crop productivity through early disease detection and providing insights into grape variety characterization in agriculture.
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18
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Allogmani AS, Mohamed RM, Al-Shibly NM, Ragab M. Enhanced cervical precancerous lesions detection and classification using Archimedes Optimization Algorithm with transfer learning. Sci Rep 2024; 14:12076. [PMID: 38802525 PMCID: PMC11130149 DOI: 10.1038/s41598-024-62773-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 05/21/2024] [Indexed: 05/29/2024] Open
Abstract
Cervical cancer (CC) ranks as the fourth most common form of cancer affecting women, manifesting in the cervix. CC is caused by the Human papillomavirus (HPV) infection and is eradicated by vaccinating women from an early age. However, limited medical facilities present a significant challenge in mid- or low-income countries. It can improve the survivability rate and be successfully treated if the CC is detected at earlier stages. Current technological improvements allow for cost-effective, more sensitive, and rapid screening and treatment measures for CC. DL techniques are widely adopted for the automated detection of CC. DL techniques and architectures are used to detect CC and provide higher detection performance. This study offers the design of Enhanced Cervical Precancerous Lesions Detection and Classification using the Archimedes Optimization Algorithm with Transfer Learning (CPLDC-AOATL) algorithm. The CPLDC-AOATL algorithm aims to diagnose cervical cancer using medical images. At the preliminary stage, the CPLDC-AOATL technique involves a bilateral filtering (BF) technique to eliminate the noise in the input images. Besides, the CPLDC-AOATL technique applies the Inception-ResNetv2 model for the feature extraction process, and the use of AOA chose the hyperparameters. The CPLDC-AOATL technique involves a bidirectional long short-term memory (BiLSTM) model for the cancer detection process. The experimental outcome of the CPLDC-AOATL technique emphasized the superior accuracy outcome of 99.53% over other existing approaches under a benchmark dataset.
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Affiliation(s)
- Ayed S Allogmani
- University of Jeddah, College of Science and Arts at Khulis, Department of Biology, Jeddah, Saudi Arabia
| | - Roushdy M Mohamed
- University of Jeddah, College of Science and Arts at Khulis, Department of Biology, Jeddah, Saudi Arabia.
| | - Nasser M Al-Shibly
- Physiotherapy Department, College of Applied Health Sciences, Jerash University, Jerash, Jordan
| | - Mahmoud Ragab
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
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19
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Pacal I. MaxCerVixT: A novel lightweight vision transformer-based Approach for precise cervical cancer detection. Knowl Based Syst 2024; 289:111482. [DOI: 10.1016/j.knosys.2024.111482] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2025]
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20
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Sikkandar MY, Sundaram SG, Alassaf A, AlMohimeed I, Alhussaini K, Aleid A, Alolayan SA, Ramkumar P, Almutairi MK, Begum SS. Utilizing adaptive deformable convolution and position embedding for colon polyp segmentation with a visual transformer. Sci Rep 2024; 14:7318. [PMID: 38538774 PMCID: PMC11377543 DOI: 10.1038/s41598-024-57993-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 03/24/2024] [Indexed: 09/07/2024] Open
Abstract
Polyp detection is a challenging task in the diagnosis of Colorectal Cancer (CRC), and it demands clinical expertise due to the diverse nature of polyps. The recent years have witnessed the development of automated polyp detection systems to assist the experts in early diagnosis, considerably reducing the time consumption and diagnostic errors. In automated CRC diagnosis, polyp segmentation is an important step which is carried out with deep learning segmentation models. Recently, Vision Transformers (ViT) are slowly replacing these models due to their ability to capture long range dependencies among image patches. However, the existing ViTs for polyp do not harness the inherent self-attention abilities and incorporate complex attention mechanisms. This paper presents Polyp-Vision Transformer (Polyp-ViT), a novel Transformer model based on the conventional Transformer architecture, which is enhanced with adaptive mechanisms for feature extraction and positional embedding. Polyp-ViT is tested on the Kvasir-seg and CVC-Clinic DB Datasets achieving segmentation accuracies of 0.9891 ± 0.01 and 0.9875 ± 0.71 respectively, outperforming state-of-the-art models. Polyp-ViT is a prospective tool for polyp segmentation which can be adapted to other medical image segmentation tasks as well due to its ability to generalize well.
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Affiliation(s)
- Mohamed Yacin Sikkandar
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia.
| | - Sankar Ganesh Sundaram
- Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, Coimbatore, 641407, India
| | - Ahmad Alassaf
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
| | - Ibrahim AlMohimeed
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
| | - Khalid Alhussaini
- Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh, 12372, Saudi Arabia
| | - Adham Aleid
- Department of Biomedical Technology, College of Applied Medical Sciences, King Saud University, Riyadh, 12372, Saudi Arabia
| | - Salem Ali Alolayan
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
| | - P Ramkumar
- Department of Computer Science and Engineering, Sri Sairam College of Engineering, Anekal, Bengaluru, 562106, Karnataka, India
| | - Meshal Khalaf Almutairi
- Department of Medical Equipment Technology, College of Applied Medical Sciences, Majmaah University, Al Majmaah, 11952, Saudi Arabia
| | - S Sabarunisha Begum
- Department of Biotechnology, P.S.R. Engineering College, Sivakasi, 626140, India
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21
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Pacal I. Enhancing crop productivity and sustainability through disease identification in maize leaves: Exploiting a large dataset with an advanced vision transformer model. EXPERT SYSTEMS WITH APPLICATIONS 2024; 238:122099. [DOI: 10.1016/j.eswa.2023.122099] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2025]
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22
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Sahafi A, Koulaouzidis A, Lalinia M. Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images. Diagnostics (Basel) 2024; 14:474. [PMID: 38472946 DOI: 10.3390/diagnostics14050474] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/26/2024] [Accepted: 02/19/2024] [Indexed: 03/14/2024] Open
Abstract
Gastrointestinal (GI) tract disorders are a significant public health issue. They are becoming more common and can cause serious health problems and high healthcare costs. Small bowel tumours (SBTs) and colorectal cancer (CRC) are both becoming more prevalent, especially among younger adults. Early detection and removal of polyps (precursors of malignancy) is essential for prevention. Wireless Capsule Endoscopy (WCE) is a procedure that utilises swallowable camera devices that capture images of the GI tract. Because WCE generates a large number of images, automated polyp segmentation is crucial. This paper reviews computer-aided approaches to polyp detection using WCE imagery and evaluates them using a dataset of labelled anomalies and findings. The study focuses on YOLO-V8, an improved deep learning model, for polyp segmentation and finds that it performs better than existing methods, achieving high precision and recall. The present study underscores the potential of automated detection systems in improving GI polyp identification.
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Affiliation(s)
- Ali Sahafi
- Department of Mechanical and Electrical Engineering, Digital and High-Frequency Electronics Section, University of Southern Denmark, 5230 Odense, Denmark
| | - Anastasios Koulaouzidis
- Surgical Research Unit, Odense University Hospital, 5000 Svendborg, Denmark
- Department of Clinical Research, University of Southern Denmark, 5230 Odense, Denmark
- Department of Medicine, OUH Svendborg Sygehus, 5700 Svendborg, Denmark
- Department of Social Medicine and Public Health, Pomeranian Medical University, 70204 Szczecin, Poland
| | - Mehrshad Lalinia
- Department of Mechanical and Electrical Engineering, Digital and High-Frequency Electronics Section, University of Southern Denmark, 5230 Odense, Denmark
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23
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Gan T, Jin Z, Yu L, Liang X, Zhang H, Ye X. Self-supervised representation learning using feature pyramid siamese networks for colorectal polyp detection. Sci Rep 2023; 13:21655. [PMID: 38066207 PMCID: PMC10709402 DOI: 10.1038/s41598-023-49057-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 12/04/2023] [Indexed: 12/18/2023] Open
Abstract
Colorectal cancer is a leading cause of cancer-related deaths globally. In recent years, the use of convolutional neural networks in computer-aided diagnosis (CAD) has facilitated simpler detection of early lesions like polyps during real-time colonoscopy. However, the majority of existing techniques require a large training dataset annotated by experienced experts. To alleviate the laborious task of image annotation and utilize the vast amounts of readily available unlabeled colonoscopy data to further improve the polyp detection ability, this study proposed a novel self-supervised representation learning method called feature pyramid siamese networks (FPSiam). First, a feature pyramid encoder module was proposed to effectively extract and fuse both local and global feature representations among colonoscopic images, which is important for dense prediction tasks like polyp detection. Next, a self-supervised visual feature representation containing the general feature of colonoscopic images is learned by the siamese networks. Finally, the feature representation will be transferred to the downstream colorectal polyp detection task. A total of 103 videos (861,400 frames), 100 videos (24,789 frames), and 60 videos (15,397 frames) in the LDPolypVideo dataset are used to pre-train, train, and test the performance of the proposed FPSiam and its counterparts, respectively. The experimental results have illustrated that our FPSiam approach obtains the optimal capability, which is better than that of other state-of-the-art self-supervised learning methods and is also higher than the method based on transfer learning by 2.3 mAP and 3.6 mAP for two typical detectors. In conclusion, FPSiam provides a cost-efficient solution for developing colorectal polyp detection systems, especially in conditions where only a small fraction of the dataset is labeled while the majority remains unlabeled. Besides, it also brings fresh perspectives into other endoscopic image analysis tasks.
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Affiliation(s)
- Tianyuan Gan
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China
| | - Ziyi Jin
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China
| | - Liangliang Yu
- Department of Gastroenterology, Endoscopy Center, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, China
| | - Xiao Liang
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, China
| | - Hong Zhang
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China
| | - Xuesong Ye
- Biosensor National Special Laboratory, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China.
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24
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Pacal I, Kılıcarslan S. Deep learning-based approaches for robust classification of cervical cancer. Neural Comput Appl 2023; 35:18813-18828. [DOI: 10.1007/s00521-023-08757-w] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 06/12/2023] [Indexed: 05/14/2025]
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25
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Fei L, Han B. Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3852. [PMID: 37112193 PMCID: PMC10144185 DOI: 10.3390/s23083852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 03/29/2023] [Accepted: 04/05/2023] [Indexed: 06/19/2023]
Abstract
Multi-Objective Multi-Camera Tracking (MOMCT) is aimed at locating and identifying multiple objects from video captured by multiple cameras. With the advancement of technology in recent years, it has received a lot of attention from researchers in applications such as intelligent transportation, public safety and self-driving driving technology. As a result, a large number of excellent research results have emerged in the field of MOMCT. To facilitate the rapid development of intelligent transportation, researchers need to keep abreast of the latest research and current challenges in related field. Therefore, this paper provide a comprehensive review of multi-object multi-camera tracking based on deep learning for intelligent transportation. Specifically, we first introduce the main object detectors for MOMCT in detail. Secondly, we give an in-depth analysis of deep learning based MOMCT and evaluate advanced methods through visualisation. Thirdly, we summarize the popular benchmark data sets and metrics to provide quantitative and comprehensive comparisons. Finally, we point out the challenges faced by MOMCT in intelligent transportation and present practical suggestions for the future direction.
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Affiliation(s)
- Lunlin Fei
- School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
- Jiangxi Provincial Transportation Investment Group Co., Ltd., Nanchang 330029, China
| | - Bing Han
- School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China;
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26
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Cabral BP, Braga LAM, Syed-Abdul S, Mota FB. Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers. Curr Oncol 2023; 30:3432-3446. [PMID: 36975473 PMCID: PMC10047823 DOI: 10.3390/curroncol30030260] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
Cancer significantly contributes to global mortality, with 9.3 million annual deaths. To alleviate this burden, the utilization of artificial intelligence (AI) applications has been proposed in various domains of oncology. However, the potential applications of AI and the barriers to its widespread adoption remain unclear. This study aimed to address this gap by conducting a cross-sectional, global, web-based survey of over 1000 AI and cancer researchers. The results indicated that most respondents believed AI would positively impact cancer grading and classification, follow-up services, and diagnostic accuracy. Despite these benefits, several limitations were identified, including difficulties incorporating AI into clinical practice and the lack of standardization in cancer health data. These limitations pose significant challenges, particularly regarding testing, validation, certification, and auditing AI algorithms and systems. The results of this study provide valuable insights for informed decision-making for stakeholders involved in AI and cancer research and development, including individual researchers and research funding agencies.
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Affiliation(s)
| | - Luiza Amara Maciel Braga
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
| | - Fabio Batista Mota
- Laboratory of Cellular Communication, Oswaldo Cruz Institute, Oswaldo Cruz Foundation, Rio de Janeiro 21040-360, Brazil
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