Classification of skin lesion using deep convolutional neural network by applying transfer learning
Journal: Journal of Autonomous Intelligence DOI: 10.32629/jai.v6i3.747
Abstract
Keywords
skin malignancy; deep learning; data augmentation; transfer learning; skin lesion; biopsy reduction; diagnosis accuracy
Funding
None
Full Text
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3. Das K, Cockerell CJ, Patil A, et al. Machine learning and its application in skin cancer. International Journal of Environmental Research and Public Health 2021; 18(24): 13409. doi: 10.3390/ijerph182413409
4. Khan MA, Akram T, Zhang YD, et al. SkinNet-ENDO: Multiclass skin lesion recognition using deep neural network and Entropy-Normal distribution optimization algorithm with ELM. International Journal of Imaging Systems and Technology 2023; 33(4): 1275–1292. doi: 10.1002/ima.22863
5. Shaukat K, Luo S, Varadharajan V, et al. Performance comparison and current challenges of using machine learning techniques in cybersecurity. Energies 2020; 13(10): 2509. doi: 10.3390/en13102509
6. Khan MA, Zhang YD, Sharif M, Akram T. Pixels to classes: Intelligent learning framework for multiclass skin lesion localization and classification. Computers & Electrical Engineering 2021; 90: 106956. doi: 10.1016/j.compeleceng.2020.106956
7. Panthakkan A, Anzar SM, Jamal S, Mansoor W. Concatenated Xception-ResNet50—A novel hybrid approach for accurate skin cancer prediction. Computers in Biology and Medicine 2022; 150: 106170. doi: 10.1016/j.compbiomed.2022.106170
8. Jeihooni AK, Harsini PA, Imani G, Hamzehie S. Melanoma epidemiology: Symptoms, causes, and preventions. In: Melanoma-Standard of Care, Challenges, and Updates in Clinical Research. IntechOpen; 2023.
9. Wollina U, Bayyoud Y, Krönert C, Nowak A. Giant epithelial malignancies (Basal cell carcinoma, squamous cell carcinoma): A series of 20 tumors from a single center. Journal of Cutaneous and Aesthetic Surgery 2012; 5(1): 12. doi: 10.4103/0974-2077.94328
10. Yoon JH, Baek EJ, Park EJ, Kim KH. Comparative study of treatment methods for benign lichenoid keratosis of the face. Dermatologic Therapy 2022; 35(5): e15419. doi: 10.1111/dth.15419
11. Yamada Y, Ichiki T, Susuki Y, et al. Diagnostic utility of ERG immunostaining in dermatofibroma. Journal of Clinical Pathology 2022; 76(8): 536–540. doi: 10.1136/jclinpath-2022-208158
12. Chaturvedi SS, Gupta K, Prasad PS. Skin lesion analyser: An efficient seven-way multi-class skin cancer classification using MobileNet. In: Advanced Machine Learning Technologies and Applications. Springer; 2021. pp. 165–176.
13. Shaukat K, Luo S, Varadharajan V, et al. A survey on machine learning techniques for cyber security in the last decade. IEEE Access 2020; 8: 222310–222354. doi: 10.1109/ACCESS.2020.3041951
14. Ali K, Shaikh ZA, Khan AA, Laghari AA. Multiclass skin cancer classification using EfficientNets—A first step towards preventing skin cancer. Neuroscience Informatics 2022; 2(4): 100034. doi: 10.1016/j.neuri.2021.100034
15. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA: A Cancer Journal for Clinicians 2022; 72(1): 7–33. doi: 10.3322/caac.21708
16. Adegun A, Viriri S. Deep learning techniques for skin lesion analysis and melanoma cancer detection: A survey of state-of-the-art. Artificial Intelligence Review 2021; 54(2): 811–841. doi: 10.1007/s10462-020-09865-y
17. Mazhar T, Haq I, Ditta A, et al. The role of machine learning and deep learning approaches for the detection of skin cancer. Healthcare 2023; 11(3): 415. doi: 10.3390/healthcare11030415
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24. Rashid J, Ishfaq M, Ali G, et al. Skin cancer disease detection using transfer learning technique. Applied Sciences 2022; 12(11): 5714. doi: 10.3390/app12115714
25. Lafraxo S, Ansari ME, Charfi S. MelaNet: An effective deep learning framework for melanoma detection using dermoscopic images. Multimedia Tools and Applications 2022; 81(11): 16021–16045. doi: 10.1007/s11042-022-12521-y
26. Ameri A. A deep learning approach to skin cancer detection in dermoscopy images. Journal of Biomedical Physics and Engineering 2020; 10(6): 801–806. doi: 10.31661/jbpe.v0i0.2004-1107
27. Wu Y, Chen B, Zeng A, et al. Skin cancer classification with deep learning: A systematic review. Frontiers in Oncology 2022; 12: 893972. doi: 10.3389/fonc.2022.893972
28. Balaha HM, Hassan AES. Skin cancer diagnosis based on deep transfer learning and sparrow search algorithm. Neural Computing and Applications 2023; 35(1): 815–853. doi: 10.1007/s00521-022-07762-9
29. Tian Y, Fu Y, Zhang J. Joint supervised and unsupervised deep learning method for single-pixel imaging. Optics & Laser Technology 2023; 162: 109278. doi: 10.1016/j.optlastec.2023.109278
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31. He X, Wang Y, Zhao S, Yao C. Deep metric attention learning for skin lesion classification in dermoscopy images. Complex & Intelligent Systems 2022; 8(2): 1487–1504. doi: 10.1007/s40747-021-00587-4
32. Mishra S, Zhang Y, Zhang L, et al. Data-driven deep supervision for skin lesion classification. In: Medical Image Computing and Computer Assisted Intervention—MICCAI 2022, Proceedings of the 25th International Conference; 18–22 September 2022; Singapore. Springer; 2022. Volume 13431, pp. 721–731.
33. Shetty B, Fernandes R, Rodrigues AP, et al. Skin lesion classification of dermoscopic images using machine learning and convolutional neural network. Scientific Reports 2022; 12(1): 18134. doi: 10.1038/s41598-022-22644-9
34. Liu Y, Jain A, Eng C, et al. A deep learning system for differential diagnosis of skin diseases. Nature Medicine 2020; 26(6): 900–908. doi: 10.1038/s41591-020-0842-3
35. Codella NCF, Gutman D, Celebi ME, et al. Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018); 4–7 April 2018; Washington, USA. pp. 168–172.
36. Harangi B, Baran A, Hajdu A. Classification of skin lesions using an ensemble of deep neural networks. In: Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 18–21 July 2018; Honolulu, USA. pp. 2575–2578.
37. Prathiba M, Jose D, Saranya R, et al. Automated melanoma recognition in dermoscopy images via very deep residual networks. In: IOP Conference Series: Materials Science and Engineering, Proceedings of the First International Conference on Materials Science and Manufacturing Technology; 12–13 April 2019; Tamil Nadu, India. IOP Publishing Ltd; 2019. Volume 561, pp. 12107.
38. Jiang S, Li H, Jin Z. A visually interpretable deep learning framework for histopathological image-based skin cancer diagnosis. IEEE Journal of Biomedical and Health Informatics 2021; 25(5): 1483–1494. doi: 10.1109/JBHI.2021.3052044
39. Fergus P, Chalmers C. Performance evaluation metrics. In: Applied Deep Learning: Tools, Techniques, and Implementation. Springer; 2022. pp. 115–138.
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41. Yacouby R, Axman D. Probabilistic extension of precision, recall, and F1 score for more thorough evaluation of classification models. In: Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems; 20 November 2020; Punta Cana, Dominica. pp. 79–91.
42. Danish J, Sellappan P, Asiah L, et al. Diagnosis of gastric cancer using machine learning techniques in healthcare sector: A survey. Informatica 2022; 45(7): 144–166. doi: 10.31449/inf.v45i7.3633
43. Jamil D, Palaniappan S, Zia SS, et al. Reducing the risk of gastric cancer through proper nutrition—A meta-analysis. International Journal of Online and Biomedical Engineering 2022; 18(7): 115–150. doi: 10.3991/ijoe.v18i07.30487
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46. Benbrahim H, Hachimi H, Amine A. Deep convolutional neural network with tensorflow and keras to classify skin cancer images. Scalable Computing Practice and Experience 2020; 21(3): 379–390. doi: 10.12694/scpe.v21i3.1725
47. Arshad M, Khan MA, Tariq U, et al. A computer-aided diagnosis system using deep learning for multiclass skin lesion classification. Computational Intelligence and Neuroscience 2021; 2021: 9619079. doi: 10.1155/2021/9619079
48. Bibi A, Khan MA, Javed MY, et al. Skin lesion segmentation and classification using conventional and deep learning based framework. Computers Materials & Continua 2022; 71(2): 2477–2495. doi: 10.32604/cmc.2022.018917
49. Khan MA, Muhammad K, Sharif M, et al. Intelligent fusion-assisted skin lesion localization and classification for smart healthcare. Neural Computing and Applications 2021; 1–16. doi: 10.1007/s00521-021-06490-w
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2. Malik S, Akram T, Awais M, et al. An improved skin lesion boundary estimation for enhanced-intensity images using hybrid metaheuristics. Diagnostics 2023; 13(7): 1285. doi: 10.3390/diagnostics13071285
3. Das K, Cockerell CJ, Patil A, et al. Machine learning and its application in skin cancer. International Journal of Environmental Research and Public Health 2021; 18(24): 13409. doi: 10.3390/ijerph182413409
4. Khan MA, Akram T, Zhang YD, et al. SkinNet-ENDO: Multiclass skin lesion recognition using deep neural network and Entropy-Normal distribution optimization algorithm with ELM. International Journal of Imaging Systems and Technology 2023; 33(4): 1275–1292. doi: 10.1002/ima.22863
5. Shaukat K, Luo S, Varadharajan V, et al. Performance comparison and current challenges of using machine learning techniques in cybersecurity. Energies 2020; 13(10): 2509. doi: 10.3390/en13102509
6. Khan MA, Zhang YD, Sharif M, Akram T. Pixels to classes: Intelligent learning framework for multiclass skin lesion localization and classification. Computers & Electrical Engineering 2021; 90: 106956. doi: 10.1016/j.compeleceng.2020.106956
7. Panthakkan A, Anzar SM, Jamal S, Mansoor W. Concatenated Xception-ResNet50—A novel hybrid approach for accurate skin cancer prediction. Computers in Biology and Medicine 2022; 150: 106170. doi: 10.1016/j.compbiomed.2022.106170
8. Jeihooni AK, Harsini PA, Imani G, Hamzehie S. Melanoma epidemiology: Symptoms, causes, and preventions. In: Melanoma-Standard of Care, Challenges, and Updates in Clinical Research. IntechOpen; 2023.
9. Wollina U, Bayyoud Y, Krönert C, Nowak A. Giant epithelial malignancies (Basal cell carcinoma, squamous cell carcinoma): A series of 20 tumors from a single center. Journal of Cutaneous and Aesthetic Surgery 2012; 5(1): 12. doi: 10.4103/0974-2077.94328
10. Yoon JH, Baek EJ, Park EJ, Kim KH. Comparative study of treatment methods for benign lichenoid keratosis of the face. Dermatologic Therapy 2022; 35(5): e15419. doi: 10.1111/dth.15419
11. Yamada Y, Ichiki T, Susuki Y, et al. Diagnostic utility of ERG immunostaining in dermatofibroma. Journal of Clinical Pathology 2022; 76(8): 536–540. doi: 10.1136/jclinpath-2022-208158
12. Chaturvedi SS, Gupta K, Prasad PS. Skin lesion analyser: An efficient seven-way multi-class skin cancer classification using MobileNet. In: Advanced Machine Learning Technologies and Applications. Springer; 2021. pp. 165–176.
13. Shaukat K, Luo S, Varadharajan V, et al. A survey on machine learning techniques for cyber security in the last decade. IEEE Access 2020; 8: 222310–222354. doi: 10.1109/ACCESS.2020.3041951
14. Ali K, Shaikh ZA, Khan AA, Laghari AA. Multiclass skin cancer classification using EfficientNets—A first step towards preventing skin cancer. Neuroscience Informatics 2022; 2(4): 100034. doi: 10.1016/j.neuri.2021.100034
15. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA: A Cancer Journal for Clinicians 2022; 72(1): 7–33. doi: 10.3322/caac.21708
16. Adegun A, Viriri S. Deep learning techniques for skin lesion analysis and melanoma cancer detection: A survey of state-of-the-art. Artificial Intelligence Review 2021; 54(2): 811–841. doi: 10.1007/s10462-020-09865-y
17. Mazhar T, Haq I, Ditta A, et al. The role of machine learning and deep learning approaches for the detection of skin cancer. Healthcare 2023; 11(3): 415. doi: 10.3390/healthcare11030415
18. Beyrami SMG, Ghaderyan P. A robust, cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis. Measurement 2020; 156: 107579. doi: 10.1016/j.measurement.2020.107579
19. Yang J, Xu R, Wang C, et al. Early screening and diagnosis strategies of pancreatic cancer: A comprehensive review. Cancer Communications 2021; 41(12): 1257–1274. doi: 10.1002/cac2.12204
20. Kalaivani A, Karpagavalli S. A deep ensemble model for automated multiclass classification using dermoscopy images. In: Proceedings of the 2022 6th International Conference on Computing Methodologies and Communication (ICCMC); 29–31 March 2022; Erode, India. pp. 1419–1423.
21. Jutzi TB, Krieghoff-Henning EI, Holland-Letz T, et al. Artificial intelligence in skin cancer diagnostics: The patients’ perspective. Frontiers in Medicine 2020; 7: 233. doi: 10.3389/fmed.2020.00233
22. Javaid A, Sadiq M, Akram F. Skin cancer classification using image processing and machine learning. In: Proceedings of the 2021 International Bhurban Conference on Applied Sciences and Technologies (IBCAST); 12–16 January 2021; Islamabad, Pakistan. pp. 439–444.
23. Adla D, Reddy GVR, Nayak P, Karuna G. Deep learning-based computer aided diagnosis model for skin cancer detection and classification. Distributed and Parallel Databases 2022; 40(4): 717–736. doi: 10.1007/s10619-021-07360-z
24. Rashid J, Ishfaq M, Ali G, et al. Skin cancer disease detection using transfer learning technique. Applied Sciences 2022; 12(11): 5714. doi: 10.3390/app12115714
25. Lafraxo S, Ansari ME, Charfi S. MelaNet: An effective deep learning framework for melanoma detection using dermoscopic images. Multimedia Tools and Applications 2022; 81(11): 16021–16045. doi: 10.1007/s11042-022-12521-y
26. Ameri A. A deep learning approach to skin cancer detection in dermoscopy images. Journal of Biomedical Physics and Engineering 2020; 10(6): 801–806. doi: 10.31661/jbpe.v0i0.2004-1107
27. Wu Y, Chen B, Zeng A, et al. Skin cancer classification with deep learning: A systematic review. Frontiers in Oncology 2022; 12: 893972. doi: 10.3389/fonc.2022.893972
28. Balaha HM, Hassan AES. Skin cancer diagnosis based on deep transfer learning and sparrow search algorithm. Neural Computing and Applications 2023; 35(1): 815–853. doi: 10.1007/s00521-022-07762-9
29. Tian Y, Fu Y, Zhang J. Joint supervised and unsupervised deep learning method for single-pixel imaging. Optics & Laser Technology 2023; 162: 109278. doi: 10.1016/j.optlastec.2023.109278
30. Shaham U, Cheng X, Dror O, et al. A deep learning approach to unsupervised ensemble learning. In: Proceedings of the 33rd International conference on machine learning; 19–24 June 2016; New York, USA. pp. 30–39.
31. He X, Wang Y, Zhao S, Yao C. Deep metric attention learning for skin lesion classification in dermoscopy images. Complex & Intelligent Systems 2022; 8(2): 1487–1504. doi: 10.1007/s40747-021-00587-4
32. Mishra S, Zhang Y, Zhang L, et al. Data-driven deep supervision for skin lesion classification. In: Medical Image Computing and Computer Assisted Intervention—MICCAI 2022, Proceedings of the 25th International Conference; 18–22 September 2022; Singapore. Springer; 2022. Volume 13431, pp. 721–731.
33. Shetty B, Fernandes R, Rodrigues AP, et al. Skin lesion classification of dermoscopic images using machine learning and convolutional neural network. Scientific Reports 2022; 12(1): 18134. doi: 10.1038/s41598-022-22644-9
34. Liu Y, Jain A, Eng C, et al. A deep learning system for differential diagnosis of skin diseases. Nature Medicine 2020; 26(6): 900–908. doi: 10.1038/s41591-020-0842-3
35. Codella NCF, Gutman D, Celebi ME, et al. Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018); 4–7 April 2018; Washington, USA. pp. 168–172.
36. Harangi B, Baran A, Hajdu A. Classification of skin lesions using an ensemble of deep neural networks. In: Proceedings of the 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 18–21 July 2018; Honolulu, USA. pp. 2575–2578.
37. Prathiba M, Jose D, Saranya R, et al. Automated melanoma recognition in dermoscopy images via very deep residual networks. In: IOP Conference Series: Materials Science and Engineering, Proceedings of the First International Conference on Materials Science and Manufacturing Technology; 12–13 April 2019; Tamil Nadu, India. IOP Publishing Ltd; 2019. Volume 561, pp. 12107.
38. Jiang S, Li H, Jin Z. A visually interpretable deep learning framework for histopathological image-based skin cancer diagnosis. IEEE Journal of Biomedical and Health Informatics 2021; 25(5): 1483–1494. doi: 10.1109/JBHI.2021.3052044
39. Fergus P, Chalmers C. Performance evaluation metrics. In: Applied Deep Learning: Tools, Techniques, and Implementation. Springer; 2022. pp. 115–138.
40. Hema V, Thota S, Kumar SN, et al. Scrum: An effective software development agile tool. In: IOP Conference Series: Materials Science and Engineering, Proceedings of the International Conference on Recent Advancements in Engineering and Management (ICRAEM-2020); 9–10 October 2020; Warangal, India. IOP Publishing Ltd; 2020. Volume 981, pp. 22060.
41. Yacouby R, Axman D. Probabilistic extension of precision, recall, and F1 score for more thorough evaluation of classification models. In: Proceedings of the First Workshop on Evaluation and Comparison of NLP Systems; 20 November 2020; Punta Cana, Dominica. pp. 79–91.
42. Danish J, Sellappan P, Asiah L, et al. Diagnosis of gastric cancer using machine learning techniques in healthcare sector: A survey. Informatica 2022; 45(7): 144–166. doi: 10.31449/inf.v45i7.3633
43. Jamil D, Palaniappan S, Zia SS, et al. Reducing the risk of gastric cancer through proper nutrition—A meta-analysis. International Journal of Online and Biomedical Engineering 2022; 18(7): 115–150. doi: 10.3991/ijoe.v18i07.30487
44. Jamil D, Palaniappan S, Debnath SK, et al. Prediction model for gastric cancer via class balancing techniques. Journal of Computer Science and Network Security 2023; 23(1): 53–63. doi: 10.22937/IJCSNS.2023.23.1.8
45. Devnath L, Luo S, Summons P, et al. Deep ensemble learning for the automatic detection of pneumoconiosis in coal worker’s chest X-ray radiography. Journal of Clinical Medicine 2022; 11(18): 5342. doi: 10.3390/jcm11185342
46. Benbrahim H, Hachimi H, Amine A. Deep convolutional neural network with tensorflow and keras to classify skin cancer images. Scalable Computing Practice and Experience 2020; 21(3): 379–390. doi: 10.12694/scpe.v21i3.1725
47. Arshad M, Khan MA, Tariq U, et al. A computer-aided diagnosis system using deep learning for multiclass skin lesion classification. Computational Intelligence and Neuroscience 2021; 2021: 9619079. doi: 10.1155/2021/9619079
48. Bibi A, Khan MA, Javed MY, et al. Skin lesion segmentation and classification using conventional and deep learning based framework. Computers Materials & Continua 2022; 71(2): 2477–2495. doi: 10.32604/cmc.2022.018917
49. Khan MA, Muhammad K, Sharif M, et al. Intelligent fusion-assisted skin lesion localization and classification for smart healthcare. Neural Computing and Applications 2021; 1–16. doi: 10.1007/s00521-021-06490-w
50. Afza F, Sharif M, Khan MA, et al. Multiclass skin lesion classification using hybrid deep features selection and extreme learning machine. Sensors 2022; 22(3): 799. doi: 10.3390/s22030799
51. Allugunti VR. A machine learning model for skin disease classification using convolution neural network. International Journal of Computing, Programming and Database Management 2022; 3(1): 141–147. doi: 10.33545/27076636.2022.v3.i1b.53
52. Salih O, Duffy KJ. Optimization convolutional neural network for automatic skin lesion diagnosis using a genetic algorithm. Applied Sciences 2023; 13(5): 3248. doi: 10.3390/app13053248
53. Benyahia S, Meftah B, Lézoray O. Multi-features extraction based on deep learning for skin lesion classification. Tissue Cell 2022; 74: 101701. doi: 10.1016/j.tice.2021.101701
54. Maqsood S, DamaÅ¡eviÄius R. Multiclass skin lesion localization and classification using deep learning based features fusion and selection framework for smart healthcare. Neural Networks 2023; 160: 238–258. doi: 10.1016/j.neunet.2023.01.022
55. Khan SH, Hayat M, Porikli F. Regularization of deep neural networks with spectral dropout. Neural Networks 2019; 110: 82–90. doi: 10.1016/j.neunet.2018.09.009
56. Kumar R, Singh D, Chug A, Singh AP. Evaluation of deep learning based resnet-50 for plant disease classification with stability analysis. In: Proceedings of the 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS); 25–27 May 2022; Madurai, India. pp. 1280–1287.
57. Duarte J, Harris P, Hauck S, et al. FPGA-accelerated machine learning inference as a service for particle physics computing. Computing Software Big Science 2019; 3: 1–15. doi: 10.1007/s41781-019-0027-2
58. Bahrampour S, Ramakrishnan N, Schott L, Shah M. Comparative study of deep learning software frameworks. arXiv 2015; arXiv:1511.06435. doi: 10.48550/arXiv.1511.06435
59. Taylor R, Ojha V, Martino I, Nicosia G. Sensitivity analysis for deep learning: Ranking hyper-parameter influence. In: Proceedings of the 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI); 1–3 November 2021; Washington, USA. pp. 512–516.
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