Enhancing Skin Cancer Diagnosis Through Optimized Convolutional Neural Network in a Smart Health Care System

Skin cancer presents one of the most complex public health problems in many countries worldwide. Accurate and time-sensitive diagnosis may help in suitable treatment and enhanced patient benefits. The study is titled as “Improving Skin Cancer Classification using Optimized Convolutional Neural Networks.” Here, it advances early diagnosis and categorization of this kind of skin cancer applying more sophisticated methodologies of artificial intelligence. Preprocessing and augmentation of the ISIC 2019 dataset improve the quality and diversity of the data set, solving problems of scarcity and imbalance. By using CNN architectures such as EfficientNetV2 and InceptionResNetV2, results have had high accuracy rates, 97.3% and 96.1%, respectively. The automated diagnostic reports with images can be downloaded as PDF files. It uses an AI-driven methodology in an enhanced way to improve diagnostic accuracy and early intervention towards better patient outcomes.