Automated Melanoma Detection: Harnessing the Power of Machine Learning for Precise Skin Disease Diagnosis

Abstract

The most dreadful disease in the world is skin cancer. Australia is sometimes regarded as the nation most severely impacted by melanoma due to its high melanoma incidence rates. Though it is very rare in India, its incidence has been gradually increasing in recent years. While melanoma is relatively less common compared to other types of skin cancers in India, it is important to recognize that the disease can still occur. Due to melanoma's rapid development, early detection is vital for effective treatment and a greater chance of survival. By exploring melanoma detection, researchers desire to minimize the strain on healthcare systems along with the emotional toll it takes on individuals and their families. Early identification may significantly boost the probability of successful intervention, potentially saving people from invasive therapies and lengthy surgeries. The state of the art for melanoma detection using machine learning algorithms is presented in detail in this survey study. This study includes melanoma detection algorithms, data gathering techniques, pre-processing methods, feature extraction strategies, and classification systems. The architecture and criteria used to assess the performance of classification algorithms, including support vector machines, K-nearest neighbour, and Convolutional Neural Networks are covered. Moreover, this survey paper examines the limitations and open challenges in the field, such as time complexity, dataset biases, and generalizability of models. We conclude by identifying promising directions for future research, including addressing the class imbalance and enhancing the interpretability of machine learning models.