Abstract
Skin cancer is a common disease and considered to be one of the most prevalent forms of cancer found in humans. Over the years various imaging techniques have shown improvement and reliability in diagnosis process of Skin Cancer. However, quite a few challenges are being faced in generating reliable and well-timed results as adoption of clinical computer aided systems is still limited. With the recent emergence of learning algorithms and its application in computer vision suggests a need for combination of sufficient clinical expertise and systems to achieve better results. Here we attempt to bridge the gap by mining collective knowledge contained in current Deep Learning Techniques to discover underlying principles for designing a neural network for skin disease classification. The solution is based upon merging of top-N performing models used as a feature extractor and a SVM to facilitate classification of diseases. Final model gave 86% accuracy on ISIC 2019 dataset along with high precision and recall values of 0.8 and 0.6, respectively.
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Bhardwaj, A., Rege, P.P. (2021). Skin Lesion Classification Using Deep Learning. In: Merchant, S.N., Warhade, K., Adhikari, D. (eds) Advances in Signal and Data Processing . Lecture Notes in Electrical Engineering, vol 703. Springer, Singapore. https://doi.org/10.1007/978-981-15-8391-9_42
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DOI: https://doi.org/10.1007/978-981-15-8391-9_42
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