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Fang X, Chong CF, Wong KL, Simões M, Ng BK. Investigating the key principles in two-step heterogeneous transfer learning for early laryngeal cancer identification. Sci Rep 2025; 15:2146. [PMID: 39820368 PMCID: PMC11739633 DOI: 10.1038/s41598-024-84836-9] [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: 10/15/2024] [Accepted: 12/27/2024] [Indexed: 01/19/2025] Open
Abstract
Data scarcity in medical images makes transfer learning a common approach in computer-aided diagnosis. Some disease classification tasks can rely on large homogeneous public datasets to train the transferred model, while others cannot, i.e., endoscopic laryngeal cancer image identification. Distinguished from most current works, this work pioneers exploring a two-step heterogeneous transfer learning (THTL) framework for laryngeal cancer identification and summarizing the fundamental principles for the intermediate domain selection. For heterogeneity and clear vascular representation, diabetic retinopathy images were chosen as THTL's intermediate domain. The experiment results reveal two vital principles in intermediate domain selection for future studies: 1) the size of the intermediate domain is not a sufficient condition to improve the transfer learning performance; 2) even distinct vascular features in the intermediate domain do not guarantee improved performance in the target domain. We observe that radial vascular patterns benefit benign classification, whereas twisted and tangled patterns align more with malignant classification. Additionally, to compensate for the absence of twisted patterns in the intermediate domains, we propose the Step-Wise Fine-Tuning (SWFT) technique, guided by the Layer Class Activate Map (LayerCAM) visualization result, getting 20.4% accuracy increases compared to accuracy from THTL's, even higher than fine-tune all layers.
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Affiliation(s)
- Xinyi Fang
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China
- Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, 3000, Portugal
| | - Chak Fong Chong
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China
- Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, 3000, Portugal
| | - Kei Long Wong
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China
- Department of Computer Science and Engineering, University of Bologna, Bologna, 40100, Italy
| | - Marco Simões
- Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, 3000, Portugal
| | - Benjamin K Ng
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, 999078, China.
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2
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Han N, Miao R, Chen D, Fan J, Chen L, Yue S, Tan T, Yang B, Wang Y. An Early Thyroid Screening Model Based on Transformer and Secondary Transfer Learning for Chest and Thyroid CT Images. Technol Cancer Res Treat 2025; 24:15330338251323168. [PMID: 40165465 PMCID: PMC11960174 DOI: 10.1177/15330338251323168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2025] Open
Abstract
IntroductionThyroid cancer is a common malignant tumor, and early diagnosis and timely treatment are crucial to improve patient prognosis. With the increasing use of enhanced CT scans, a new opportunity for early thyroid cancer screening has emerged. However, existing CT-based models face challenges due to limited datasets, small sample sizes, and high noise.MethodsTo address these challenges, we collected enhanced CT scan image data from 240 patients in Guangdong and Xinjiang, China, and established a CT dataset for early thyroid cancer screening. We propose a deep learning model, the DVT model, which combines transformer DNN and transfer learning techniques to integrate time series data and address small sample sizes and high noise.ResultsThe experimental results show that the DVT model achieves a prediction accuracy of 0.96, AUROC of 0.97, specificity of 1, and sensitivity of 0.94. These results indicate that the DVT model is a highly effective tool for early thyroid cancer screening.ConclusionThe DVT model has the potential to assist clinicians in identifying potential thyroid cancer patients and reducing patient expenses. Our study provides a new approach to thyroid cancer screening using enhanced CT scans and demonstrates the effectiveness of deep learning techniques in addressing the challenges associated with CT-based models.
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Affiliation(s)
- Na Han
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, P. R. China
- Business School, Beijing Institute of Technology, Zhuhai, P. R. China
| | - Rui Miao
- Basic Teaching Department, Zhuhai Campus of Zunyi Medical University, Zhu Hai, P. R. China
| | - Dongwei Chen
- Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, P. R. China
| | - Jinrui Fan
- Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, P. R. China
| | - Lin Chen
- Basic Teaching Department, Zhuhai Campus of Zunyi Medical University, Zhu Hai, P. R. China
| | - Siyao Yue
- Basic Teaching Department, Zhuhai Campus of Zunyi Medical University, Zhu Hai, P. R. China
| | - Tao Tan
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, P. R. China
| | - Bowen Yang
- Image Center, The First People's Hospital of Kashi, Xinjiang, P. R. China
| | - Yapeng Wang
- Faculty of Applied Sciences, Macao Polytechnic University, Macao, P. R. China
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3
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Wang J, Yang X, Jia X, Xue W, Chen R, Chen Y, Zhu X, Liu L, Cao Y, Zhou J, Ni D, Gu N. Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training. Comput Biol Med 2024; 171:108087. [PMID: 38364658 DOI: 10.1016/j.compbiomed.2024.108087] [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: 10/01/2023] [Revised: 01/04/2024] [Accepted: 01/27/2024] [Indexed: 02/18/2024]
Abstract
Thyroid nodule classification and segmentation in ultrasound images are crucial for computer-aided diagnosis; however, they face limitations owing to insufficient labeled data. In this study, we proposed a multi-view contrastive self-supervised method to improve thyroid nodule classification and segmentation performance with limited manual labels. Our method aligns the transverse and longitudinal views of the same nodule, thereby enabling the model to focus more on the nodule area. We designed an adaptive loss function that eliminates the limitations of the paired data. Additionally, we adopted a two-stage pre-training to exploit the pre-training on ImageNet and thyroid ultrasound images. Extensive experiments were conducted on a large-scale dataset collected from multiple centers. The results showed that the proposed method significantly improves nodule classification and segmentation performance with limited manual labels and outperforms state-of-the-art self-supervised methods. The two-stage pre-training also significantly exceeded ImageNet pre-training.
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Affiliation(s)
- Jian Wang
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China
| | - Xin Yang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518073, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, 518073, China; Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, 518073, China
| | - Xiaohong Jia
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China
| | - Wufeng Xue
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518073, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, 518073, China; Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, 518073, China
| | - Rusi Chen
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518073, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, 518073, China; Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, 518073, China
| | - Yanlin Chen
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518073, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, 518073, China; Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, 518073, China
| | - Xiliang Zhu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518073, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, 518073, China; Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, 518073, China
| | - Lian Liu
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518073, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, 518073, China; Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, 518073, China
| | - Yan Cao
- Shenzhen RayShape Medical Technology Co., Ltd, Shenzhen, 518051, China
| | - Jianqiao Zhou
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China.
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518073, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, 518073, China; Medical UltraSound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, 518073, China.
| | - Ning Gu
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China; Cardiovascular Disease Research Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Medical School, Nanjing University, Nanjing, 210093, China.
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Li J, Jiang P, An Q, Wang GG, Kong HF. Medical image identification methods: A review. Comput Biol Med 2024; 169:107777. [PMID: 38104516 DOI: 10.1016/j.compbiomed.2023.107777] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/30/2023] [Accepted: 11/28/2023] [Indexed: 12/19/2023]
Abstract
The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging provided a good scheme for medical image analysis over traditional methods that rely on the handcrafted features, it remains challenging due to the diversity of imaging modalities and clinical pathologies. Many medical image identification methods provide a good scheme for medical image analysis. The concepts pertinent of methods, such as the machine learning, deep learning, convolutional neural networks, transfer learning, and other image processing technologies for medical image are analyzed and summarized in this paper. We reviewed these recent studies to provide a comprehensive overview of applying these methods in various medical image analysis tasks, such as object detection, image classification, image registration, segmentation, and other tasks. Especially, we emphasized the latest progress and contributions of different methods in medical image analysis, which are summarized base on different application scenarios, including classification, segmentation, detection, and image registration. In addition, the applications of different methods are summarized in different application area, such as pulmonary, brain, digital pathology, brain, skin, lung, renal, breast, neuromyelitis, vertebrae, and musculoskeletal, etc. Critical discussion of open challenges and directions for future research are finally summarized. Especially, excellent algorithms in computer vision, natural language processing, and unmanned driving will be applied to medical image recognition in the future.
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Affiliation(s)
- Juan Li
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China; School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
| | - Pan Jiang
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China
| | - Qing An
- School of Artificial Intelligence, Wuchang University of Technology, Wuhan, 430223, China
| | - Gai-Ge Wang
- School of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China.
| | - Hua-Feng Kong
- School of Information Engineering, Wuhan Business University, Wuhan, 430056, China.
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Tan Z, Yu Y, Meng J, Liu S, Li W. Self-supervised learning with self-distillation on COVID-19 medical image classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107876. [PMID: 37875036 DOI: 10.1016/j.cmpb.2023.107876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 10/11/2023] [Accepted: 10/17/2023] [Indexed: 10/26/2023]
Abstract
BACKGROUND AND OBJECTIVE Currently, COVID-19 is a highly infectious disease that can be clinically diagnosed based on diagnostic radiology. Deep learning is capable of mining the rich information implied in inpatient imaging data and accomplishing the classification of different stages of the disease process. However, a large amount of training data is essential to train an excellent deep-learning model. Unfortunately, due to factors such as privacy and labeling difficulties, annotated data for COVID-19 is extremely scarce, which encourages us to propose a more effective deep learning model that can effectively assist specialist physicians in COVID-19 diagnosis. METHODS In this study,we introduce Masked Autoencoder (MAE) for pre-training and fine-tuning directly on small-scale target datasets. Based on this, we propose Self-Supervised Learning with Self-Distillation on COVID-19 medical image classification (SSSD-COVID). In addition to the reconstruction loss computation on the masked image patches, SSSD-COVID further performs self-distillation loss calculations on the latent representation of the encoder and decoder outputs. The additional loss calculation can transfer the knowledge from the global attention of the decoder to the encoder which acquires only local attention. RESULTS Our model achieves 97.78 % recognition accuracy on the SARS-COV-CT dataset containing 2481 images and is further validated on the COVID-CT dataset containing 746 images, which achieves 81.76 % recognition accuracy. Further introduction of external knowledge resulted in experimental accuracies of 99.6% and 95.27 % on these two datasets, respectively. CONCLUSIONS SSSD-COVID can obtain good results on the target dataset alone, and when external information is introduced, the performance of the model can be further improved to significantly outperform other models.Overall, the experimental results show that our method can effectively mine COVID-19 features from rare data and can assist professional physicians in decision-making to improve the efficiency of COVID-19 disease detection.
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Affiliation(s)
- Zhiyong Tan
- School of Computer Science and Engineering, Dalian Minzu University, Dalian, Liaoning 116600, China
| | - Yuhai Yu
- School of Computer Science and Engineering, Dalian Minzu University, Dalian, Liaoning 116600, China
| | - Jiana Meng
- School of Computer Science and Engineering, Dalian Minzu University, Dalian, Liaoning 116600, China.
| | - Shuang Liu
- School of Computer Science and Engineering, Dalian Minzu University, Dalian, Liaoning 116600, China
| | - Wei Li
- School of Computer Science and Engineering, Dalian Minzu University, Dalian, Liaoning 116600, China
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Nahiduzzaman M, Faruq Goni MO, Robiul Islam M, Sayeed A, Shamim Anower M, Ahsan M, Haider J, Kowalski M. Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture. Biocybern Biomed Eng 2023; 43:S0208-5216(23)00037-2. [PMID: 38620111 PMCID: PMC10292668 DOI: 10.1016/j.bbe.2023.06.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 04/04/2023] [Accepted: 06/16/2023] [Indexed: 11/09/2023]
Abstract
Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lung-related diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives. To fight against lung diseases, timely and correct diagnosis with appropriate treatment is crucial in the current COVID-19 pandemic. In this study, an intelligent recognition system for seven lung diseases has been proposed based on machine learning (ML) techniques to aid the medical experts. Chest X-ray (CXR) images of lung diseases were collected from several publicly available databases. A lightweight convolutional neural network (CNN) has been used to extract characteristic features from the raw pixel values of the CXR images. The best feature subset has been identified using the Pearson Correlation Coefficient (PCC). Finally, the extreme learning machine (ELM) has been used to perform the classification task to assist faster learning and reduced computational complexity. The proposed CNN-PCC-ELM model achieved an accuracy of 96.22% with an Area Under Curve (AUC) of 99.48% for eight class classification. The outcomes from the proposed model demonstrated better performance than the existing state-of-the-art (SOTA) models in the case of COVID-19, pneumonia, and tuberculosis detection in both binary and multiclass classifications. For eight class classification, the proposed model achieved precision, recall and fi-score and ROC are 100%, 99%, 100% and 99.99% respectively for COVID-19 detection demonstrating its robustness. Therefore, the proposed model has overshadowed the existing pioneering models to accurately differentiate COVID-19 from the other lung diseases that can assist the medical physicians in treating the patient effectively.
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Affiliation(s)
- Md Nahiduzzaman
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Omaer Faruq Goni
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Robiul Islam
- Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Abu Sayeed
- Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Md Shamim Anower
- Department of Electrical & Electronic Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh
| | - Mominul Ahsan
- Department of Computer Science, University of York, Deramore Lane, Heslington, York YO10 5GH, UK
| | - Julfikar Haider
- Department of Engineering, Manchester Metropolitan University, Chester St, Manchester M1 5GD, UK
| | - Marcin Kowalski
- Institute of Optoelectronics, Military University of Technology, Gen. S. Kaliskiego 2, Warsaw, Poland
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Patel RK, Kashyap M. Machine learning- based lung disease diagnosis from CT images using Gabor features in Littlewood Paley empirical wavelet transform (LPEWT) and LLE. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING: IMAGING & VISUALIZATION 2023. [DOI: 10.1080/21681163.2023.2187244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Affiliation(s)
- Rajneesh Kumar Patel
- Department of Electronics & Communication, Maulana Azad National Institute of Technology, Bhopal (M.P.), India
| | - Manish Kashyap
- Department of Electronics & Communication, Maulana Azad National Institute of Technology, Bhopal (M.P.), India
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Shibu George G, Raj Mishra P, Sinha P, Ranjan Prusty M. COVID-19 Detection on Chest X-Ray Images Using Homomorphic Transformation and VGG Inspired Deep Convolutional Neural Network. Biocybern Biomed Eng 2022; 43:1-16. [PMID: 36447948 PMCID: PMC9684127 DOI: 10.1016/j.bbe.2022.11.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 11/01/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022]
Abstract
COVID-19 had caused the whole world to come to a standstill. The current detection methods are time consuming as well as costly. Using Chest X-rays (CXRs) is a solution to this problem, however, manual examination of CXRs is a cumbersome and difficult process needing specialization in the domain. Most of existing methods used for this application involve the usage of pretrained models such as VGG19, ResNet, DenseNet, Xception, and EfficeintNet which were trained on RGB image datasets. X-rays are fundamentally single channel images, hence using RGB trained model is not appropriate since it increases the operations by involving three channels instead of one. A way of using pretrained model for grayscale images is by replicating the one channel image data to three channel which introduces redundancy and another way is by altering the input layer of pretrained model to take in one channel image data, which comprises the weights in the forward layers that were trained on three channel images which weakens the use of pre-trained weights in a transfer learning approach. A novel approach for identification of COVID-19 using CXRs, Contrast Limited Adaptive Histogram Equalization (CLAHE) along with Homomorphic Transformation Filter which is used to process the pixel data in images and extract features from the CXRs is suggested in this paper. These processed images are then provided as input to a VGG inspired deep Convolutional Neural Network (CNN) model which takes one channel image data as input (grayscale images) to categorize CXRs into three class labels, namely, No-Findings, COVID-19, and Pneumonia. Evaluation of the suggested model is done with the help of two publicly available datasets; one to obtain COVID-19 and No-Finding images and the other to obtain Pneumonia CXRs. The dataset comprises 6750 images in total; 2250 images for each class. Results obtained show that the model has achieved 96.56% for multi-class classification and 98.06% accuracy for binary classification using 5-fold stratified cross validation (CV) method. This result is competitive and up to the mark when compared with the performance shown by existing approaches for COVID-19 classification.
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Affiliation(s)
- Gerosh Shibu George
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India
| | - Pratyush Raj Mishra
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India
| | - Panav Sinha
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India
| | - Manas Ranjan Prusty
- Centre for Cyber Physical Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu 600127, India
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The internet of medical things and artificial intelligence: trends, challenges, and opportunities. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.05.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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