Deep Attention Fusion Framework for Automated Monkeypox Skin Image Classification

The main objective of this project is to develop a deep learning-based system for monkeypox skin image classification. The first objective is to use the Monkeypox Skin Image Dataset for training and evaluating the selected models. The second objective is to implement EfficientNetViTCBAM for advanced feature extraction and attention-based classification. This model helps the system focus on important regions in the skin image and reduce the effect of less useful background information. The third objective is to implement DenseNet121 as a comparative model to analyze classification performance. Another objective is to build a simple web application using Flask, HTML, CSS, and JavaScript, where users can register, log in, upload an image, and view the classification result. The project also aims to maintain a clear workflow through separate modules such as Home, Register, Login, Classification, and Logout. In addition, the system aims to provide a user-friendly interface and organized backend processing. Overall, the objective is to create an accurate, understandable, and research-oriented monkeypox skin image classification system using modern deep learning methods.