An internet of health things‐driven deep learning framework for detection and classification of skin cancer using transfer learning

Abstract

Abstract As specified by World Health Organization, the occurrence of skin cancer has been growing over the past decades. At present, 2 to 3 million nonmelanoma skin cancers and 132 000 melanoma skin cancers arise worldwide annually. The detection and classification of skin cancer in early stage of development allow patients to have proper diagnosis and treatment. The goal of this article is to present a novel deep learning internet of health and things (IoHT) driven framework for skin lesion classification in skin images using the concept of transfer learning. In proposed framework, automatic features are extracted from images using different pretrained architectures like VGG19, Inception V3, ResNet50, and SqueezeNet, which are fed into fully connected layer of convolutional neural network for classification of skin benign and malignant cells using dense and max pooling operation. In addition, the proposed system is fully integrated with an IoHT framework and can be used remotely to assist medical specialists in the diagnosis and treatment of skin cancer. It has been observed that performance metric evaluation of proposed framework outperformed other pretrained architectures in term of precision, recall, and accuracy in detection and classification of skin cancer from skin lesion images.

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