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Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art

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Abstract

Analysis of skin lesion images via visual inspection and manual examination to diagnose skin cancer has always been cumbersome. This manual examination of skin lesions in order to detect melanoma can be time-consuming and tedious. With the advancement in technology and rapid increase in computational resources, various machine learning techniques and deep learning models have emerged for the analysis of medical images most especially the skin lesion images. The results of these models have been impressive, however analysis of skin lesion images with these techniques still experiences some challenges due to the unique and complex features of the skin lesion images. This work presents a comprehensive survey of techniques that have been used for detecting skin cancer from skin lesion images. The paper is aimed to provide an up-to-date survey that will assist investigators in developing efficient models that automatically and accurately detects melanoma from skin lesion images. The paper is presented in five folds: First, we identify the challenges in detecting melanoma from skin lesions. Second, we discuss the pre-processing and segmentation techniques of skin lesion images. Third, we make comparative analysis of the state-of-the-arts. Fourth we discuss classification techniques for classifying skin lesions into different classes of skin cancer. We finally explore and analyse the performance of the state-of-the-arts methods employed in popular skin lesion image analysis competitions and challenges of ISIC 2018 and 2019. Application of ensemble deep learning models on well pre-processed and segmented images results in better classification performance of the skin lesion images.

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    Adekanmi Adegun & Serestina Viriri

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Adegun, A., Viriri, S. Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art. Artif Intell Rev 54, 811–841 (2021). https://doi.org/10.1007/s10462-020-09865-y

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