Analysis of basic neural network types for automated skin cancer classification using Firefly optimization method
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ORIGINAL RESEARCH
Analysis of basic neural network types for automated skin cancer classification using Firefly optimization method M. Sundar Prakash Balaji1 · S. Saravanan2 · M. Chandrasekar3 · G. Rajkumar3 · S. Kamalraj4 Received: 27 April 2020 / Accepted: 23 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In recent days, cancer is a deadly disease because of its spreading nature to other cells, and this disease is not identified at an early detection stage. Generally, the cancer is detected with the help of a biopsy method, which is a painful approach. Due to the development of technology, nowadays, it is identified with the help of image processing methods. Here, the image processing approach is used for identifying and classifying the skin cancer types, namely melanoma, common and atypical nevi. The methods used earlier for the detection and classification are artificial skin leison merging, Raman spectroscopy and back-propagation networks. Cancer is classified into many types like blood cancer, bone, colon, and stomach and skin cancer. Among these cancer types, skin cancer can be a dreadful disease, which is detected and then treated at the starting stage of the disease. Hence, this paper proposed an optimized neural and fuzzy approach for skin cancer classification. The fuzzy c-means segmentation is used for the detection of the cancer region. Firefly optimization determines the dominant feature for the training of the neural network. The dominant feature is determined by reducing the error rate of the classifier. The overall process is evaluated with the help of evaluation metrics like accuracy, specificity and sensitivity. In this proposed method, the best result is achieved for the pattern net by improving its accuracy by 4.9% from its previous Moth-Flame Optimization based classification in its evaluation. Keywords Cancer · Melanoma · Fuzzy C means · Firefly optimization · Error rate · Neural network types · Accuracy · Mean · Sensitivity
1 Introduction Due to advancements in technology and programming concepts, the necessity of human intervention in predicting the information based on the data is reduced with the help of Electronic supplementary material The online version of this article (https://doi.org/10.1007/s12652-020-02394-0) contains supplementary material, which is available to authorized users. * G. Rajkumar [email protected]; [email protected] 1
Department of EEE, RVS College of Engineering and Technology, Coimbatore, Tamilnadu, India
2
Department of Electronics and Communication Engineering, Srinivasa Ramanujan Centre, SASTRA Deemed University, Kumbakonam, India
3
Department of ECE, School of EEE, SASTRA Deemed University, Thanjavur, India
4
Department of ECE, Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, India
machine learning concepts. This machine learning depends on the data given by the user in the training of the network. This machine learning can be used for various applications like segmentation, classi
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