Classification and Detection of Skin Disease Based on Machine Learning and Image Processing Evolutionary Models
Abstract
Skin disorders, a prevalent cause of illnesses, may be identified by studying their physical structure and history of the condition. Currently, skin diseases are diagnosed using invasive procedures such as clinical examination and histology. The examinations are quite effective and beneficial. This paper describes an evolutionary model for skin disease classification and detection based on machine learning and image processing. This model integrates image preprocessing, image augmentation, segmentation, and machine learning algorithms. The experimental investigation makes use of a dermatology data set. The model employs the machine learning methods: the support vector machine (SVM), the k-nearest neighbors (KNN), and random forest algorithms for image categorization and detection. This suggested methodology is beneficial for the accurate identification of skin disease using image analysis. The SVM algorithm achieved an accuracy of 98.8%. The KNN algorithm achieved a sensitivity of 91%. The specificity of KNN was 99%.
Keywords:
skin disorders, machine learning, classification, image enchancement, image segmentation, disease detectionReferences
1. M.A.M. Almeida, I.A.X. Santos, Classification models for skin tumor detection using texture analysis in medical images, Journal of Imaging, 6(6): 51, 2020, https://doi.org/10.3390/jimaging6060051
2. A. Raghuvanshi, U.K. Singh, C. Joshi, A review of various security and privacy innovations for IoT applications in healthcare, [in:] Advanced Healthcare Systems, R. Tanwar, S. Balamurugan, R.K. Saini, V. Bharti, P. Chithaluru [Eds.], Chapter 4, pp. 43–58, 2022, https://doi.org/10.1002/9781119769293.ch4
3. V. Durga Prasad Jasti et al., Computational technique based on machine learning and image processing for medical image analysis of breast cancer diagnosis, Security and Communication Networks, 2022: Article ID 1918379, 2022, https://doi.org/10.1155/2022/1918379
4. W. Sae-Lim, W. Wettayaprasit, P. Aiyarak, Convolutional neural networks using MobileNet for skin lesion classification, [in:] Proceedings of the 16th International Joint Conference on Computer Science and Software Engineering, Chonburi, Thailand, 10–12 July, pp. 242–247, 2019, https://doi.org/10.1109/JCSSE.2019.8864155
5. D. Castillo, V. Lakshminarayanan, M.J. Rodríguez-Álvarez, MR images, brain lesions, and deep learning, Applied Sciences, 11(4): 1675, 2021, https://doi.org/10.3390/app11041675
6. A. Raghuvanshi et al., Intrusion detection using machine learning for risk mitigation in IoT-enabled smart irrigation in smart farming, Journal of Food Quality, 2022: Article ID 3955514, 2022, https://doi.org/10.1155/2022/3955514
7. V. Hemamalini et al., Food quality inspection and grading using efficient image segmentation and machine learning-based system, Journal of Food Quality, 2022: Article ID 5262294, 2022, https://doi.org/10.1155/2022/5262294
8. L. Bajaj, K. Gupta, Y. Hasija, Image processing in biomedical science, [in:] Advances in Soft Computing and Machine Learning in Image Processing, A.E. Hassanien, D.A. Oliva [Eds.], pp. 185–211, 2017, https://doi.org/10.1007/978-3-319-63754-9_9
9. L.S. Wei, Q. Gan, T. Ji, Skin disease recognition method based on image color and texture features, Computational and Mathematical Methods in Medicine, 2018: 8145713, 2018, https://doi.org/10.1155/2018/8145713
10. M.N. Islam, J. Gallardo-Alvarado, M. Abu, N.A. Salman, S.P. Rengan, S. Said, Skin disease recognition using texture analysis, [in:] IEEE 8th Control and System Graduate Research Colloquium (ICSGRC), pp. 144–148, 2017, https://doi.org/10.1109/ICSGRC.2017.8070584
11. H. Liao, Y. Li, J. Luo, Skin disease classification versus skin lesion characterization: Achieving robust diagnosis using multi-label deep neural networks, [in:] 23rd International Conference on Pattern Recognition (ICPR), pp. 355–360, 2016, https://doi.org/10.1109/ICPR.2016.7899659
12. S. Kumar, A. Singh, Image processing for recognition of skin diseases, International Journal of Computer Applications, 149(3): 37–40, 2016, https://doi.org/10.5120/ijca2016911373
13. Deepalakshmi P., Prudhvi Krishna T., SiriChandana S., Lavanya K., P. Srinivasu, Plant leaf disease detection using CNN algorithm, International Journal of Information System Modeling and Design, 12(1): 1, 21 pages, 2021, https://doi.org/10.4018/ijismd.2021010101
14. P. Naga Srinivasu, T. Srinivasa Rao, A.M. Dicu, C.A. Mnerie, I. Olariu, A comparative review of optimisation techniques in segmentation of brain MR images, Journal of Intelligent & Fuzzy Systems, 38(5): 6031–6043, 2020, https://doi.org/10.3233/jifs-179688
15. A. Kumar, J. Kim, D. Lyndon, M. Fulham, D. Feng, An ensemble of fine-tuned convolutional neural networks for medical image classification, IEEE Journal of Biomedical and Health Informatics, 21(1): 31–40, 2017, https://doi.org/10.1109/JBHI.2016.2635663
16. S. Kolkur, D.R. Kalbande, Survey of texture based feature extraction for skin disease detection, [in:] 2016 International Conference on ICT in Business Industry & Government, pp. 1–6, 2016, https://doi.org/10.1109/ICTBIG.2016.7892649
17. B. Harangi, Skin lesion classification with ensembles of deep convolutional neural networks, Journal of Biomedical Informatics, 86(1): 25–32, 2018, https://doi.org/10.1016/j.jbi.2018.08.006
18. J. Naranjo-Torres, M. Mora, R. Hernández-García, R.J. Barrientos, C. Fredes, A. Valenzuela, A review of convolutional neural network applied to fruit image processing, Applied Sciences, 10(10): 3443, 2020, https://doi.org/10.3390/app10103443
19. A.K. Verma, S. Pal, S. Kumar, Classification of skin disease using ensemble data mining techniques, Asian Pacific Journal of Cancer Prevention, 20(6): 1887–1894, 2019, https://doi.org/10.31557/apjcp.2019.20.6.1887
20. X. Zhang, S. Wang, J. Liu, C. Tao, Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge, BMC Medical Informatics and Decision Making, 18(Suppl 2): Article number 59, 2018, https://doi.org/10.1186/s12911-018-0631-9
21. S.S. Yadav, S.M. Jadhav, Deep convolutional neural network based medical image classification for disease diagnosis, Journal of Big Data, 6(1): Article number 113, 2019, https://doi.org/10.1186/s40537-019-0276-2
22. A.K. Gupta, C. Chakraborty, B. Gupta, Secure transmission of EEG data using watermarking algorithm for the detection of epileptical seizures, Traitement du Signal, 38(2): 473–479, 2021, https://doi.org/10.18280/ts.380227
23. C. Wu, P. Lu, F. Xu, J. Duan, X. Hua, M. Shabaz, The prediction models of anaphylactic disease, Informatics in Medicine Unlocked, 24: 100535, 2021, https://doi.org/10.1016/j.imu.2021.100535
24. A. Gupta, P. Bharat, Novel approaches in network fault management, International Journal of Next-Generation Computing, 8(2): 115–126, 2017, https://doi.org/10.47164/ijngc.v8i2.126
25. N. Reza, I.S. Na, S.W. Baek, K.-H. Lee, Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images, Biosystems Engineering, 177: 109–121, 2019, https://doi.org/10.1016/j.biosystemseng.2018.09.014
26. A. Kapoor, A. Gupta, R. Gupta, S. Tanwar, G. Sharma, I.E. Davidson, Ransomware detection, avoidance, and mitigation scheme: A review and future directions, Sustainability, 14(1): 8, 2021, https://doi.org/10.3390/su14010008
27. M. Shabaz, U. Garg, Predicting future diseases based on existing health status using link prediction, World Journal of Engineering, 19(1): 29–32, 2021, https://doi.org/10.1108/wje-10-2020-0533
28. H.Q. Yu, S. Reiff-Marganiec, Targeted ensemble machine classification approach for supporting IoT enabled skin disease detection, IEEE Access, 9: 50244–50252, 2021, https://doi.org/10.1109/ACCESS.2021.3069024
29. Dermatology Data Set, [in:] D. Dua, C. Graff, UCI Machine Learning Repository, School of Information and Computer Science, University of California, Irvine, CA, 2019, https://archive.ics.uci.edu/ml/datasets/Dermatology
2. A. Raghuvanshi, U.K. Singh, C. Joshi, A review of various security and privacy innovations for IoT applications in healthcare, [in:] Advanced Healthcare Systems, R. Tanwar, S. Balamurugan, R.K. Saini, V. Bharti, P. Chithaluru [Eds.], Chapter 4, pp. 43–58, 2022, https://doi.org/10.1002/9781119769293.ch4
3. V. Durga Prasad Jasti et al., Computational technique based on machine learning and image processing for medical image analysis of breast cancer diagnosis, Security and Communication Networks, 2022: Article ID 1918379, 2022, https://doi.org/10.1155/2022/1918379
4. W. Sae-Lim, W. Wettayaprasit, P. Aiyarak, Convolutional neural networks using MobileNet for skin lesion classification, [in:] Proceedings of the 16th International Joint Conference on Computer Science and Software Engineering, Chonburi, Thailand, 10–12 July, pp. 242–247, 2019, https://doi.org/10.1109/JCSSE.2019.8864155
5. D. Castillo, V. Lakshminarayanan, M.J. Rodríguez-Álvarez, MR images, brain lesions, and deep learning, Applied Sciences, 11(4): 1675, 2021, https://doi.org/10.3390/app11041675
6. A. Raghuvanshi et al., Intrusion detection using machine learning for risk mitigation in IoT-enabled smart irrigation in smart farming, Journal of Food Quality, 2022: Article ID 3955514, 2022, https://doi.org/10.1155/2022/3955514
7. V. Hemamalini et al., Food quality inspection and grading using efficient image segmentation and machine learning-based system, Journal of Food Quality, 2022: Article ID 5262294, 2022, https://doi.org/10.1155/2022/5262294
8. L. Bajaj, K. Gupta, Y. Hasija, Image processing in biomedical science, [in:] Advances in Soft Computing and Machine Learning in Image Processing, A.E. Hassanien, D.A. Oliva [Eds.], pp. 185–211, 2017, https://doi.org/10.1007/978-3-319-63754-9_9
9. L.S. Wei, Q. Gan, T. Ji, Skin disease recognition method based on image color and texture features, Computational and Mathematical Methods in Medicine, 2018: 8145713, 2018, https://doi.org/10.1155/2018/8145713
10. M.N. Islam, J. Gallardo-Alvarado, M. Abu, N.A. Salman, S.P. Rengan, S. Said, Skin disease recognition using texture analysis, [in:] IEEE 8th Control and System Graduate Research Colloquium (ICSGRC), pp. 144–148, 2017, https://doi.org/10.1109/ICSGRC.2017.8070584
11. H. Liao, Y. Li, J. Luo, Skin disease classification versus skin lesion characterization: Achieving robust diagnosis using multi-label deep neural networks, [in:] 23rd International Conference on Pattern Recognition (ICPR), pp. 355–360, 2016, https://doi.org/10.1109/ICPR.2016.7899659
12. S. Kumar, A. Singh, Image processing for recognition of skin diseases, International Journal of Computer Applications, 149(3): 37–40, 2016, https://doi.org/10.5120/ijca2016911373
13. Deepalakshmi P., Prudhvi Krishna T., SiriChandana S., Lavanya K., P. Srinivasu, Plant leaf disease detection using CNN algorithm, International Journal of Information System Modeling and Design, 12(1): 1, 21 pages, 2021, https://doi.org/10.4018/ijismd.2021010101
14. P. Naga Srinivasu, T. Srinivasa Rao, A.M. Dicu, C.A. Mnerie, I. Olariu, A comparative review of optimisation techniques in segmentation of brain MR images, Journal of Intelligent & Fuzzy Systems, 38(5): 6031–6043, 2020, https://doi.org/10.3233/jifs-179688
15. A. Kumar, J. Kim, D. Lyndon, M. Fulham, D. Feng, An ensemble of fine-tuned convolutional neural networks for medical image classification, IEEE Journal of Biomedical and Health Informatics, 21(1): 31–40, 2017, https://doi.org/10.1109/JBHI.2016.2635663
16. S. Kolkur, D.R. Kalbande, Survey of texture based feature extraction for skin disease detection, [in:] 2016 International Conference on ICT in Business Industry & Government, pp. 1–6, 2016, https://doi.org/10.1109/ICTBIG.2016.7892649
17. B. Harangi, Skin lesion classification with ensembles of deep convolutional neural networks, Journal of Biomedical Informatics, 86(1): 25–32, 2018, https://doi.org/10.1016/j.jbi.2018.08.006
18. J. Naranjo-Torres, M. Mora, R. Hernández-García, R.J. Barrientos, C. Fredes, A. Valenzuela, A review of convolutional neural network applied to fruit image processing, Applied Sciences, 10(10): 3443, 2020, https://doi.org/10.3390/app10103443
19. A.K. Verma, S. Pal, S. Kumar, Classification of skin disease using ensemble data mining techniques, Asian Pacific Journal of Cancer Prevention, 20(6): 1887–1894, 2019, https://doi.org/10.31557/apjcp.2019.20.6.1887
20. X. Zhang, S. Wang, J. Liu, C. Tao, Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge, BMC Medical Informatics and Decision Making, 18(Suppl 2): Article number 59, 2018, https://doi.org/10.1186/s12911-018-0631-9
21. S.S. Yadav, S.M. Jadhav, Deep convolutional neural network based medical image classification for disease diagnosis, Journal of Big Data, 6(1): Article number 113, 2019, https://doi.org/10.1186/s40537-019-0276-2
22. A.K. Gupta, C. Chakraborty, B. Gupta, Secure transmission of EEG data using watermarking algorithm for the detection of epileptical seizures, Traitement du Signal, 38(2): 473–479, 2021, https://doi.org/10.18280/ts.380227
23. C. Wu, P. Lu, F. Xu, J. Duan, X. Hua, M. Shabaz, The prediction models of anaphylactic disease, Informatics in Medicine Unlocked, 24: 100535, 2021, https://doi.org/10.1016/j.imu.2021.100535
24. A. Gupta, P. Bharat, Novel approaches in network fault management, International Journal of Next-Generation Computing, 8(2): 115–126, 2017, https://doi.org/10.47164/ijngc.v8i2.126
25. N. Reza, I.S. Na, S.W. Baek, K.-H. Lee, Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images, Biosystems Engineering, 177: 109–121, 2019, https://doi.org/10.1016/j.biosystemseng.2018.09.014
26. A. Kapoor, A. Gupta, R. Gupta, S. Tanwar, G. Sharma, I.E. Davidson, Ransomware detection, avoidance, and mitigation scheme: A review and future directions, Sustainability, 14(1): 8, 2021, https://doi.org/10.3390/su14010008
27. M. Shabaz, U. Garg, Predicting future diseases based on existing health status using link prediction, World Journal of Engineering, 19(1): 29–32, 2021, https://doi.org/10.1108/wje-10-2020-0533
28. H.Q. Yu, S. Reiff-Marganiec, Targeted ensemble machine classification approach for supporting IoT enabled skin disease detection, IEEE Access, 9: 50244–50252, 2021, https://doi.org/10.1109/ACCESS.2021.3069024
29. Dermatology Data Set, [in:] D. Dua, C. Graff, UCI Machine Learning Repository, School of Information and Computer Science, University of California, Irvine, CA, 2019, https://archive.ics.uci.edu/ml/datasets/Dermatology
