Automated Detection and Classification of Skin Cancer Using Convolutional Neural Networks.

The main objective of the project is to build an intelligent system that can assist healthcare professionals by automatically detecting and classifying skin cancer from dermoscopic images. Skin cancer, one of the most common and dangerous types of cancer, can be effectively treated if detected early. However, manual diagnosis is time-consuming, subject to human error, and requires specialized expertise. This project aims to bridge that gap by automating the detection process using deep learning models, specifically CNNs. The system will be trained on large datasets of skin lesion images and will learn to differentiate between malignant and benign skin conditions. By the end of the project, students are expected to have developed a fully functional image classification model capable of delivering accurate diagnostic support. They will gain hands-on experience in image preprocessing, model training and validation, performance evaluation, and developing a simple interface for uploading and analyzing images.

To complete the project, students will engage in a series of well-structured tasks spread over twelve weeks. In the first week, they will be introduced to the fundamental concepts of Convolutional Neural Networks and understand their relevance to image classification problems. In the second week, students will begin gathering and preparing necessary image data, ideally from publicly available sources such as the ISIC archive. In the following weeks, they will learn about various deep learning libraries like TensorFlow and PyTorch, which are essential for building and training CNN models. A basic model framework will be created and trained using preprocessed dermoscopic images. Once a working model is developed, students will validate it using different datasets and tune it to improve its accuracy through optimization techniques.

In later weeks, the focus will shift to integrating the model into a user interface that allows users to upload images and receive classification results. The model's performance will be tested thoroughly, and detailed documentation will be prepared outlining the design, methodology, and results of the project. Finally, the students will present the completed project in a team setting. Throughout the project, students must maintain ethical coding standards, avoid plagiarism, and follow a clear documentation style. They are also expected to use a fully functional PC or laptop with necessary open-source software installed, and optionally, a decent camera module if image acquisition is part of the extended functionality.