Development of Computational Models in Creating Skin Disease Classifiers Using CNN and ResNet Architecture

Abstract

This research paper delves into the exploration of various computational models utilized in creating skin disease classifiers, aiming to enhance diagnostic accuracy and optimize potential accessibility to isolated patients. By investigating the different types of computational models employed in this context, this paper aims to shed light on how these models contribute to more accessible and efficient diagnoses of skin conditions, enabling healthcare professionals to provide widespread patient care. Furthermore, the paper will address the challenges encountered in the development of effective skin disease classifiers using computational models, such as data quality issues, model interpretability, and generalizability across imbalanced datasets. Through a comprehensive analysis of these aspects, this research endeavors to advance our understanding of the potential of computational models in improving skin disease diagnosis and ultimately enhancing healthcare outcomes for individuals affected by dermatological conditions. Results showed that ResNet18 demonstrated higher overall accuracy on HAM10000 when compared to the confusion matrix and Grad-CAM visualizations of both models.