Abstract
Analysis of skin lesion images is very crucial in melanoma detection. Melanoma is a form of skin cancer with high mortality rate. Both semi and fully automated systems have been proposed in the recent past for analysis of skin lesions and detection of melanoma. These systems have however been restricted in performance due to the complex visual characteristics of the skin lesions. Skin lesions images are characterised with fuzzy borders, low contrast between lesions and the background, variability in size and resolution and with possible presence of noise and artefacts. In this work, an efficient deep learning framework has been proposed for melanoma lesion detection and segmentation. The proposed method performs pixel-wise classification of skin lesion images to identify melanoma pixels. The framework employs an end-to-end and pixel by pixels learning approach using Deep Convolutional Networks with softmax classifier. A novel framework which learns the complex visual characteristics of skin lesions via an encoder and decoder subnetworks that are connected through a series of skip pathways that brings the semantic level of the encoder feature maps closer to that of the decoder feature maps is hereby designed. This efficiently handles multi-size, multi-resolution and noisy skin lesion images. The proposed system was evaluated on both the ISBI 2018 and PH2 skin lesion datasets.
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Adegun, A., Viriri, S. (2020). Deep Convolutional Network-Based Framework for Melanoma Lesion Detection and Segmentation. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_5
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