Hybrid deep learning-based skin cancer classification with RPO-SegNet for skin lesion segmentation.

Abstract

Skin melanin lesions are typically identified as tiny patches on the skin, which are impacted by melanocyte cell overgrowth. The number of people with skin cancer is increasing worldwide. Accurate and timely skin cancer identification is critical to reduce the mortality rates. An incorrect diagnosis can be fatal to the patient. To tackle these issues, this article proposes the Recurrent Prototypical Object Segmentation Network (RPO-SegNet) for the segmentation of skin lesions and a hybrid Deep Learning (DL) - based skin cancer classification. The RPO-SegNet is formed by integrating the Recurrent Prototypical Networks (RP-Net), and Object Segmentation Networks (O-SegNet). At first, the input image is taken from a database and forwarded to image pre-processing. Then, the segmentation of skin lesions is accomplished using the proposed RPO-SegNet. After the segmentation, feature extraction is accomplished. Finally, skin cancer classification and detection are accomplished by employing the Fuzzy-based Shepard Convolutional Maxout Network (FSCMN) by combining the Deep Maxout Network (DMN), and Shepard Convolutional Neural Network (ShCNN). The established RPO-SegNet+FSCMN attained improved accuracy, True Negative Rate (TNR), True Positive Rate (TPR), dice coefficient, Jaccard coefficient, and segmentation analysis of 91.985%, 92.735%, 93.485%, 90.902%, 90.164%, and 91.734%.