Abstract
The advancement of modern technology has enabled the diagnosis of various skin diseases through image processing. Researchers face significant challenges when it comes to utilize image processing tools for skin disease analysis. One particularly serious disease is Melanoma, a type of cancer originating from melanocytes. The primary objective of this research article is to develop a machine learning classification-based algorithm for melanoma detection.
The complexities associated with analyzing melanoma skin disease can be mitigated by employing an effective classification technique. In this proposed model, two machine learning (ML) classification algorithms, namely the Probabilistic Neural Network (PNN) and Support Vector Machine (SVM), are utilized for disease detection. These algorithms are employed to differentiate between melanoma-affected skin and normal skin.
To establish the machine learning algorithms, feature selection is performed using Factor Analysis (FA), and the dermenetNZ.org dataset is utilized. The performance of the two classification algorithms is compared using standard performance metrics. Through comparative analysis, it is demonstrated that the Probabilistic Neural Network classifier outperforms the Support Vector Machine in the classification of melanoma skin disease. Overall, this research article showcases the efficacy of a machine learning-based approach for melanoma detection, with the Probabilistic Neural Network exhibiting superior results compared to the Support Vector Machine classifier.
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References
Nurhudatiana, A.: A computer-aided diagnosis system for vitiligo assessment: a segmentation algorithm. In: Intan, R., Chi, CH., Palit, H., Santoso, L. (eds.) ICSIIT 2015. CCIS, vol. 516, pp. 323–331. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46742-8_30
Das, N., Pal, A., Mazumder, S., Sarkar, S., Gangopadhyay, D., Nasipuri, M.: An SVM based skin disease identification using Local Binary Patterns 978-0-7695-5033-6/13 $26.00. In: Third International Conference on Advances in Computing and Communications. IEEE (2013). https://doi.org/10.1109/ICACC.2013.48
Surovi, N.A., Kiber, A., Kashem, A., Babi, K.N.: Study and development of algorithm of different skin diseases analysis using image processing method. Asian J. Biomed. Pharm. Sci. 7(60), 1–3 (2017)
Wei, L., Gan, Q., Ji, T.: Skin disease recognition method based on image color and texture features. Hindawi Comput. Math. Methods Med., 1–10 (2018). Article no. 8145713, 10 p.
Kolkur, S., Kalbande, D.R., Kharkar, V.: Machine learning approaches to multi-class human skin disease detection. Int. J. Comput. Intell. Res. 14(1), 29–39 (2018). ISSN 0973-1873
Kumar, S., Singh, A.: Image processing for recognition of skin diseases. Int. J. Comput. Appl. 149(3), 37–40 (2016)
Lu, J., Kazmierczak, E., Jonathan, H.: Automatic segmentation of scaling in 2-D psoriasis skin images. IEEE Trans. Med. Imaging 32(4), 719–730 (2013)
Arivazhagan, S., Shebiah, R.N., Divya, K., Subadevi, M.P.: Skin disease classification by extracting independent components. J. Emerg. Trends Comput. Inf. Sci. 3(10), 1379–1382 (2012)
Cui, Q., McIntosh, S., Sun, H.: Identifying materials of photographic images and photorealistic computer-generated graphics based on deep CNNs. CMC Comput. Mater. Continua 055(2), 229–241 (2018)
Suer, S., Kockara, S., Mete, M.: An improved border detection in dermoscopy images for density based clustering. BMC Bioinform. 12(S10) (2011)
Amaliah, B., Fatichah, C., Rahmat Widyanto, M.: ABCD feature extraction for melanoma skin cancer diagnosis. Int. J. Med. Imaging, 1–8
Relethford, J.H.: Human skin color diversity is highest in sub-Saharan African populations. Hum. Biol. Int. Rec. Res. 72(5), 773–80 (2000)
Nguyen, N.H., Lee, T.K., Stella Atkins, M.: Segmentation of light and dark hair in dermoscopic images: a hybrid approach using a universal kernel. Int. J. Comput. Technol., 42–50
Specht, D.F.: Probabilistic Neural Networks. Neural Netw. 3(1,1990), 109–118 (1990)
Yasir, R., Rahman, Md.A., Ahmed, N.: Dermatological disease detection using image processing and artificial neural network. In: 8th International Conference on Electrical and Computer Engineering, December 2014, Dhaka, Bangladesh, pp. 687–690 (2014)
Parikha, K.S., Shahb, T.P.: Support vector machine – a large margin classifier to diagnose skin illnesses. In: 3rd International Conference on Innovations in Automation and Mechatronics Engineering, ICIAME 2016 (2016). https://doi.org/10.1016/j.protcy.2016.03.039
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 121–167 (1998)
Zanaty, E.A.: Support vector machine versus multilayer perception (MLP) in data classification. Egypt. Inform. J., 177–183 (2012)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995). https://doi.org/10.1007/978-1-4757-2440-0
JaseemaYasmin, J.H., Mohamed Sadiq, M.: An improved iterative segmentation algorithm using canny edge detector with iterative median filter for skin lesion border detection. Int. J. Comput. Appl. (IJCA) 50(6), 37–42 (2012). ISSN: 0975-8887
Übeyli, E.D., Doğdu, E.: Automatic detection of erythemato-squamous diseases using k-means clustering. J. Med. Syst. 34(2), 179–184 (2010)
Relethford, J.: Human skin color diversity is highest in sub-Saharan African populations. Hum. Biol. Int. Rec. Res. 72(5), 773–80 (2000)
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Barman, M., Choudhury, J.P., Biswas, S. (2024). Automated Detection of Melanoma Skin Disease Using Classification Algorithm. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1955. Springer, Cham. https://doi.org/10.1007/978-3-031-48876-4_14
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