The primary goal of the project is to develop a deep learning model that can analyze medical images and identify cancerous patterns using Convolutional Neural Networks (CNNs). Early diagnosis of cancer is vital for effective treatment, but manual image analysis can be time-consuming and prone to human error. This project seeks to address that challenge by leveraging CNNs proven effective in computer vision tasks to detect subtle anomalies in medical scans that may indicate cancer. The model will be trained on labeled image datasets and refined using techniques like transfer learning. Although it will not replace professional medical assessment, this tool can serve as a valuable aid to radiologists and healthcare providers, improving accuracy and speeding up diagnoses.
The project follows a structured twelve-week schedule. In the initial weeks, students will set up their development environment using Python, Anaconda, or Google Colab and explore relevant machine learning libraries such as TensorFlow and Keras. They will prepare datasets consisting of labeled cancer and non-cancer images and begin with designing a basic CNN architecture.
In the middle stages, students will train the model, evaluate its performance on unseen data, and improve its accuracy through hyperparameter tuning and data augmentation. Transfer learning techniques may be applied to improve efficiency and generalization. During the final weeks, students will test the model, finalize the system integration, document all processes, and deliver a team presentation. While the model can enhance diagnostic support, it must be used in conjunction with clinical expertise and cannot independently diagnose cancer.
