Automating Dermatological Diagnosis - A Comparative Analysis

Evolving and adapting healthcare along with growing technology is an urgent need of an hour using machine learning is an addition to classify health data for the large aspect of identifying dermatological illness. Convolution Neural Network (CNN) can accurately classify a variety of skin illnesses by extracting complex information from images. CNN do away with the requirement for manual feature engineering because they are able to learn from large amounts of data. They are resistant to noise and changes in lighting, ensuring accurate analysis every time. CNN help in early detection, increasing the efficacy of treatment, and lowering the possibility of a false positive. Their combination has the potential to greatly improve patient care and change dermatological procedures. Objective: The goal of this study is to determine the best effective algorithm, whether it comes from deep learning or machine learning, based on a thorough comparative analysis that considers elements like accuracy, interpret ability, and scalability. Methodology: This study uses a comparative analysis methodology to decide which machine learning and deep learning algorithms are the most effective. It entails a review of the literature, the gathering of data, the design and execution of experiments, statistical analysis, and result interpretation. Findings: This study’s findings shows that deep learning approaches are more effective than machine learning ones. In contrast to machine learning, which depends on manual, subjective techniques, deep learning allows for automatic and objective feature extraction. Deep learning achieves greater prediction accuracy and generalization ability despite requiring more training time and computational resources, demonstrating its superior learning powers.