Enhancing Skin Cancer Detection: A Study on Feature Selection Methods for Image Classification

Visually similar images can be easily identified by the human eye; however, expert knowledge is required to accurately interpret medical images, particularly those depicting skin affected by cancer.As skin cancer is becoming more commonplace worldwide, there is a growing need for qualified specialists to help with its diagnosis. Severalintricate genetic abnormalities lead to cancer, one of the most serious illnesses. Skin cancer is the most frequently diagnosed type of cancer. The present research examines two main methods: segmentation and feature extraction, since early identificationis essential to enhancing treatment results. Our research focuseson identifying malignant melanoma, which is caused by an overabundance of melanocytes in the dermis layer of the skin. We used the well-known dermatological approach known as Asymmetry, Border, Color, and Differential(ABCD) dermo copyto aid in early identification. Asymmetry (differences in shape and structure), border irregularity (uneven or jagged borders), colorvariation (differing pigmentation inside the lesion), and differential structure (development in size and appearance over time) are the criteria used in this technique to analyzeskin lesions. CNN-based deep learning models are used for image pre-processing, segmentation, feature extraction, and classification in the organizedprocess of the suggested framework. Additionally, sophisticated digital image processing methods like size estimates, coloridentification, border analysis, and symmetry detection are included. By using CNNs to collect texture-based information, feature extraction is improved and skin lesions can be precisely categorized. We suggest using a Backpropagation Neural Network (BPNN) to increase classification accuracy and make efficient decisions when distinguishing between benign and malignant skin diseases.To overcome this difficulty, machine learning classifiers have surfaced as a viable way to automate the classification of images for skin cancer. In this paper, deep Convolutional Neural Networks (CNNs) are used to construct a predictive model for skin cancer diagnosis. Usingthe HAM10000 dataset, the suggested method produced a 92% accuracy rate.