Abstract
Skin melanoma is considered as a deadliest form of skin malformation originates in human community. Due to its increasing incidence rates, it is necessary to build an accompanying procedure to assist the clinical detection and diagnosis process. Visual examination and the digital dermoscopy are the two common procedures widely adopted by the doctors to detect and verify skin melanoma. This paper proposes a soft-computing assisted tool to investigate the skin melanoma images. In this work, bat algorithm-assisted Kapur’s multithresholding is considered to preprocess the image, and the level set-based segmentation is adopted in the postprocessing stage to mine the skin melanoma section. The experimental investigation is implemented using the benchmark DERMIS dataset. The effectiveness of proposed technique is confirmed by measuring the familiar image similarity measures through a relative study among extracted skin melanoma with the ground truth. The experimental result verifies that the proposed technique is easy to implement and offers superior values of Jaccard (0.8805), Dice (0.9138), sensitivity (0.9927), specificity (0.9177), and accuracy (0.9628).
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Rajinikanth, V., Satapathy, S.C., Dey, N., Fernandes, S.L., Manic, K.S. (2019). Skin Melanoma Assessment Using Kapur’s Entropy and Level Set—A Study with Bat Algorithm. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 104. Springer, Singapore. https://doi.org/10.1007/978-981-13-1921-1_19
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