Abstract
According to the Skin Cancer Foundation statistics, skin cancer is known to be the most common cancer in the United States and worldwide. By the age of seventy years, about twenty percent of Americans will have developed skin cancer due to exposure to radiation. Of all the types of skin cancers, melanoma is particularly deadly and responsible for most skin cancer deaths. Therefore, early detection is the key to survival. An automatic skin lesion diagnosis system can assist dermatologists since its challenging to differentiate between the different classes of skin lesions. In this paper, we propose a transfer learning based deep learning system using deep convolutional neural networks that leverage residual connections to perform the mentioned task with high accuracy. The HAM10000 dataset was utilized for training and testing the model and comparing its performance with other pre-trained models. This kind of automated classification system can be integrated into a computer- aided diagnosis (CAD) system pipeline to assist in the early detection of skin cancer.
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Kollipara, V.N.H., Kollipara, V.N.D.P. (2022). Residual Learning Based Approach for Multi-class Classification of Skin Lesion Using Deep Convolutional Neural Network. In: Guru, D.S., Y. H., S.K., K., B., Agrawal, R.K., Ichino, M. (eds) Cognition and Recognition. ICCR 2021. Communications in Computer and Information Science, vol 1697. Springer, Cham. https://doi.org/10.1007/978-3-031-22405-8_27
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