Segmenting and classifying skin lesions using a fruit fly optimization algorithm with a machine learning framework

Abstract

The deadliest forms of skin cancer, melanomas have a large fatality rate. In the United States of America, 196,060 new cases of melanoma are anticipated in 2020. In the past, many automated methods for diagnosing skin lesions have been proposed, but they have not yet proven to be very accurate. Based on skin cells’ exposure to sunlight, aberrant skin cell development frequently results in skin cancer. Ultraviolet radiation, viruses, bacteria, chemicals, and fungi are the main contributors to skin conditions. The creation of a precise computer-aided system for diagnosing breast cancer is of tremendous clinical importance. An improved machine learning framework has been developed in this research to detect skin lesions or skin cancer. Hence it is important to segment and classify the skin lesion. The research utilizes the fruit fly optimization algorithm and machine learning framework to segment and classifies skin disease and cancer. This platform's central idea is to use the fruit fly optimization algorithm (FOA) to improve two crucial SVM variables and create an FOA-based SVM (FOA-SVM) for the diagnosis of skin cancer. The integrative approach not only improves accuracy but also provides important data for more accurate classification.