A full-resolution convolutional network with a dynamic graph cut algorithm for skin cancer classification and detection

Abstract

A robust medical decision support system for classifying skin lesions from dermoscopy images is a crucial instrument for determining skin cancer prognosis. In recent years, full resolution convolutional network has made significant progress in recognizing skin cancer types from Dermoscopic images despite their fine-grained changes in appearance. Recently, full resolution convolutional network have gained popularity as a solution to semantic segmentation issues. However, the hyper-parameters it chooses are what determine how well it performs, and manually fine-tuning these hyper-parameters takes time. Therefore, a hyper-parameter optimized full resolution convolutional network is suggested for dermoscopy picture segmentation in this research. The network’s hyper-parameters are optimized by a brand-new dynamic graph cut algorithm method. Hyper-parameters emphasize the proper balance between exploration and exploitation by combining the wolves’ individual haunting tactics with their global haunting strategies to generate a neighborhood-based searching strategy. The fundamental objective of this study is to develop a hyper-parameter-optimized Full resolution convolutional network-based model capable of reliably diagnosing skin cancer types using dermoscopy images. The computer-aided diagnosis could be more efficient and precise. The segmentation approach is the primary way to identify cancerous tumors with precision. This study introduces a dynamic graph cut algorithm -based method for accurate segmentation and improved skin cancer classification using a full resolution convolutional network. Experiments reveal that the proposed model effectively addresses the frequent over-segmentation and under-segmentation issues in graph cut and the subject of wrongly segmented small sections in the grab cut method. In addition, the results illustrate the utility of data augmentation in training and testing, with enhanced performance compared to the usage of fresh images. Multiple experiments were done using various transferring models, and the results of our recommended model showed superior performance in skin lesion categorization tasks relative to other architectures with an accuracy of 97.986%.