Certain Investigations on Skin Cancer Detection Through Learning Algorithms

Abstract

Skin cancer is a serious and life-threatening medical condition caused by newlineabnormal cell growth in the skin. Early detection and an accurate diagnosis are critical newlineto effective treatment. The manual approach is time-consuming and prone to inner newlineobservation variability, necessitating Computer Aided diagnosis approach. Computer newlineAided diagnosis approaches provide a systematic, objective, and automated approach to newlineskin cancer. This approach is faster and less prone to inner observation variability, newlinemaking it more reliable and efficient. Over the past three decades, so many research newlineworks have been proposed for skin cancer diagnosis. An evaluation of learning models newlinein medical images demonstrates their performance in computer vision. Still, there is so newlinemuch room to improve prediction accuracy, model explainability, and data scarcity newlineissues in skin cancer diagnosis. This research work aims to improve prediction newlineaccuracy, clinical usable model, and data scarcity in model training in order to minimize newlinethe impact of errors in prediction. Skin cancer prediction can be solved by using newlinedifferent learning applications such as segmentation, classification and detection. As a newlinepart of our first work, we introduce a novel classification method known as Improved newlineAdaboost-based Aphid-Ant Mutualism (IAB-AAM), which helps to distinguish benign newlinefrom malignant conditions by analyzing dermoscopic images of skin lesions. As a newlineresult, the model can identify and categorize dermoscopic images of skin cancer, newlinedistinguishing between benign and malignant categories of cases, in a systematic and newlinereliable manner. It computes several performance metrics like accuracy, specificity, newlineprecision, recall, f-measure, and ROC to evaluate the effectiveness of the proposed newlinemethod newline