Abstract
Skin cancer is among the most prominent types of cancer, which if not detected in the initial stages can metastasize and become fatal. As the number of Melanoma cases surges, the interest in using CAD (computer-aided diagnosis) for skin cancer prognosis is also rising. In this work, the proposed methodology is applied to HAM 10,000 dataset containing skin lesion images. This paper aims to implement neural network architectures on pre-processed segmented lesions for labeling the lesion image as benign or malignant. The images from the dataset are first pre-processed to remove the noise present, and then segmentation is performed to separate skin lesion from the background. Textural features are extracted from the segmented lesion, which are later used by perceptron and multilayer perceptron (MLP) for classification. Pre-processed images are classified using transfer learning models (ResNet-50, Inception-V3, MobileNet, Inception-ResNet-V2, and DenseNet201), where DenseNet201 has given the best performance and achieved an accuracy of 93.24% and AUC of 0.932.
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Upadhyay, M., Rawat, J., Maji, S. (2022). Skin Cancer Image Classification Using Deep Neural Network Models. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_44
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