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Angeline J, Siva Kailash A, Karthikeyan J, Karthika R, Saravanan V. Automated Prediction of Malignant Melanoma using Two-Stage Convolutional Neural Network. Arch Dermatol Res 2024; 316:275. [PMID: 38796546 DOI: 10.1007/s00403-024-03076-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: 01/22/2024] [Revised: 01/22/2024] [Accepted: 04/26/2024] [Indexed: 05/28/2024]
Abstract
PURPOSE A skin lesion refers to an area of the skin that exhibits anomalous growth or distinctive visual characteristics compared to the surrounding skin. Benign skin lesions are noncancerous and generally pose no threat. These irregular skin growths can vary in appearance. On the other hand, malignant skin lesions correspond to skin cancer, which happens to be the most prevalent form of cancer in the United States. Skin cancer involves the unusual proliferation of skin cells anywhere on the body. The conventional method for detecting skin cancer is relatively more painful. METHODS This work involves the automated prediction of skin cancer and its types using two stage Convolutional Neural Network (CNN). The first stage of CNN extracts low level features and second stage extracts high level features. Feature selection is done using these two CNN and ABCD (Asymmetry, Border irregularity, Colour variation, and Diameter) technique. The features extracted from the two CNNs are fused with ABCD features and fed into classifiers for the final prediction. The classifiers employed in this work include ensemble learning methods such as gradient boosting and XG boost, as well as machine learning classifiers like decision trees and logistic regression. This methodology is evaluated using the International Skin Imaging Collaboration (ISIC) 2018 and 2019 dataset. RESULTS As a result, the first stage CNN which is used for creation of new dataset achieved an accuracy of 97.92%. Second stage CNN which is used for feature selection achieved an accuracy of 98.86%. Classification results are obtained for both with and without fusion of features. CONCLUSION Therefore, two stage prediction model achieved better results with feature fusion.
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Affiliation(s)
- J Angeline
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India
| | - A Siva Kailash
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India
| | - J Karthikeyan
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India
| | - R Karthika
- Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India.
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Wang H, Cheng J, Li D, Luo S, Wang X, Liu Y, Wang M, Ren T. Endobronchial pigmented mass in a patient with primary malignant melanoma of the lung: A case report. Oncol Lett 2023; 26:517. [PMID: 37927412 PMCID: PMC10623094 DOI: 10.3892/ol.2023.14104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/03/2023] [Indexed: 11/07/2023] Open
Abstract
Malignant melanoma (MM) commonly presents as a primary skin tumor and respiratory MM cases are almost all metastatic. Primary lung MM (PMML) is quite rare, especially when manifested as an endobronchial pigmented mass, its diagnosis is relatively difficult and MM has a poor prognosis. Only a few cases have been described previously and the pathologic features, clinical behavior and therapeutic options are not well established. The present study reports the case of a 72-year-old female patient with PMML who denied any history of tumors. The patient complained of chest pain and coughing for 2 weeks. Chest computed tomography (CT) revealed a mass in the right upper lobe and an enlarged mediastinal lymph node. Positron emission tomogram-CT suggested a hypermetabolic tumor. To confirm the diagnosis, the patient underwent a transbronchial forceps biopsy and endobronchial ultrasound-guided transbronchial needle aspiration, which confirmed the diagnosis of PMML. Genetic testing identified a BRAF V600E mutation, so the patient received treatment with dabrafenib plus trametinib. PMML is extremely rare and is easily misdiagnosed as lung cancer due to its nonspecific clinical manifestations and imaging features. The diagnosis of PMML remains challenging due to its morphologic and immunophenotypic variability. Targeted therapy is a good option for advanced PMML patients with BRAF V600E mutations.
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Affiliation(s)
- Hansheng Wang
- Department of Pulmonary and Critical Care Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei 442000, P.R. China
| | - Jun Cheng
- Department of Pulmonary and Critical Care Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei 442000, P.R. China
| | - Dan Li
- Department of Pathology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei 442000, P.R. China
| | - Senyuan Luo
- Department of Pathology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei 442000, P.R. China
| | - Xiao Wang
- Department of Pulmonary and Critical Care Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei 442000, P.R. China
| | - Yan Liu
- Department of Pulmonary and Critical Care Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei 442000, P.R. China
| | - Meifang Wang
- Department of Pulmonary and Critical Care Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei 442000, P.R. China
| | - Tao Ren
- Department of Pulmonary and Critical Care Medicine, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei 442000, P.R. China
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Patel RH, Foltz EA, Witkowski A, Ludzik J. Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review. Cancers (Basel) 2023; 15:4694. [PMID: 37835388 PMCID: PMC10571810 DOI: 10.3390/cancers15194694] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/05/2023] [Accepted: 09/19/2023] [Indexed: 10/15/2023] Open
Abstract
BACKGROUND Melanoma, the deadliest form of skin cancer, poses a significant public health challenge worldwide. Early detection is crucial for improved patient outcomes. Non-invasive skin imaging techniques allow for improved diagnostic accuracy; however, their use is often limited due to the need for skilled practitioners trained to interpret images in a standardized fashion. Recent innovations in artificial intelligence (AI)-based techniques for skin lesion image interpretation show potential for the use of AI in the early detection of melanoma. OBJECTIVE The aim of this study was to evaluate the current state of AI-based techniques used in combination with non-invasive diagnostic imaging modalities including reflectance confocal microscopy (RCM), optical coherence tomography (OCT), and dermoscopy. We also aimed to determine whether the application of AI-based techniques can lead to improved diagnostic accuracy of melanoma. METHODS A systematic search was conducted via the Medline/PubMed, Cochrane, and Embase databases for eligible publications between 2018 and 2022. Screening methods adhered to the 2020 version of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Included studies utilized AI-based algorithms for melanoma detection and directly addressed the review objectives. RESULTS We retrieved 40 papers amongst the three databases. All studies directly comparing the performance of AI-based techniques with dermatologists reported the superior or equivalent performance of AI-based techniques in improving the detection of melanoma. In studies directly comparing algorithm performance on dermoscopy images to dermatologists, AI-based algorithms achieved a higher ROC (>80%) in the detection of melanoma. In these comparative studies using dermoscopic images, the mean algorithm sensitivity was 83.01% and the mean algorithm specificity was 85.58%. Studies evaluating machine learning in conjunction with OCT boasted accuracy of 95%, while studies evaluating RCM reported a mean accuracy rate of 82.72%. CONCLUSIONS Our results demonstrate the robust potential of AI-based techniques to improve diagnostic accuracy and patient outcomes through the early identification of melanoma. Further studies are needed to assess the generalizability of these AI-based techniques across different populations and skin types, improve standardization in image processing, and further compare the performance of AI-based techniques with board-certified dermatologists to evaluate clinical applicability.
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Affiliation(s)
- Raj H. Patel
- Edward Via College of Osteopathic Medicine, VCOM-Louisiana, 4408 Bon Aire Dr, Monroe, LA 71203, USA
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA (A.W.); (J.L.)
| | - Emilie A. Foltz
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA (A.W.); (J.L.)
- Elson S. Floyd College of Medicine, Washington State University, Spokane, WA 99202, USA
| | - Alexander Witkowski
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA (A.W.); (J.L.)
| | - Joanna Ludzik
- Department of Dermatology, Oregon Health & Science University, Portland, OR 97239, USA (A.W.); (J.L.)
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Gouda W, Sama NU, Al-Waakid G, Humayun M, Jhanjhi NZ. Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning. Healthcare (Basel) 2022; 10:healthcare10071183. [PMID: 35885710 PMCID: PMC9324455 DOI: 10.3390/healthcare10071183] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/13/2022] [Accepted: 06/15/2022] [Indexed: 12/12/2022] Open
Abstract
An increasing number of genetic and metabolic anomalies have been determined to lead to cancer, generally fatal. Cancerous cells may spread to any body part, where they can be life-threatening. Skin cancer is one of the most common types of cancer, and its frequency is increasing worldwide. The main subtypes of skin cancer are squamous and basal cell carcinomas, and melanoma, which is clinically aggressive and responsible for most deaths. Therefore, skin cancer screening is necessary. One of the best methods to accurately and swiftly identify skin cancer is using deep learning (DL). In this research, the deep learning method convolution neural network (CNN) was used to detect the two primary types of tumors, malignant and benign, using the ISIC2018 dataset. This dataset comprises 3533 skin lesions, including benign, malignant, nonmelanocytic, and melanocytic tumors. Using ESRGAN, the photos were first retouched and improved. The photos were augmented, normalized, and resized during the preprocessing step. Skin lesion photos could be classified using a CNN method based on an aggregate of results obtained after many repetitions. Then, multiple transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were used for fine-tuning. In addition to experimenting with several models (the designed CNN, Resnet50, InceptionV3, and Inception Resnet), this study’s innovation and contribution are the use of ESRGAN as a preprocessing step. Our designed model showed results comparable to the pretrained model. Simulations using the ISIC 2018 skin lesion dataset showed that the suggested strategy was successful. An 83.2% accuracy rate was achieved by the CNN, in comparison to the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models.
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Affiliation(s)
- Walaa Gouda
- Department of Computer Engineering and Network, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia
- Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 4272077, Egypt
- Correspondence: (W.G.); (M.H.)
| | - Najm Us Sama
- Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan 94300, Malaysia;
| | - Ghada Al-Waakid
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia;
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Al Jouf, Saudi Arabia
- Correspondence: (W.G.); (M.H.)
| | - Noor Zaman Jhanjhi
- School of Computer Science and Engineering (SCE), Taylor’s University, Subang Jaya 47500, Malaysia;
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Thapar P, Rakhra M, Cazzato G, Hossain MS. A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:1709842. [PMID: 35480147 PMCID: PMC9038388 DOI: 10.1155/2022/1709842] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/12/2022] [Accepted: 04/01/2022] [Indexed: 02/08/2023]
Abstract
Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals' visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques' results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.
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Affiliation(s)
- Puneet Thapar
- 1Department of Computer Science and Engineering, Lovely Professional University, Punjab, India
| | - Manik Rakhra
- 1Department of Computer Science and Engineering, Lovely Professional University, Punjab, India
| | - Gerardo Cazzato
- 2Section of Pathology, Department of Emergency and Organ Transplantation (DETO), University of Bari Aldo Moro, Bari BA, Italy
| | - Md Shamim Hossain
- 3Department of Marketing, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
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