The primary goal of this project is to develop an intelligent model that detects tumors in medical images using Convolutional Neural Networks (CNNs). Cancer detection through manual image interpretation can be time-consuming and may vary depending on the expertise of medical professionals. This project addresses that limitation by training CNNs on labeled datasets to recognize patterns indicative of tumor presence. The system aims to assist doctors in identifying tumors more accurately and efficiently, improving clinical outcomes. Though the tool will not replace expert medical judgment, it will act as a decision support system, offering faster diagnostics and second-opinion validation.
The project follows a twelve-week structured timeline, starting with the installation and setup of Python and associated machine learning libraries, using Anaconda Navigator or Google Colab. In the initial weeks, students will build foundational knowledge in CNNs and machine learning and learn to use popular tools such as TensorFlow, Keras, and OpenCV.
They will then gather and preprocess raw or unlabeled medical image datasets, followed by training the CNN model and evaluating its performance on unseen images. As development progresses, students will refine the model to improve its accuracy, complete testing, document their workflow, and present the final solution as a team. Deliverables include the trained model, prediction results, and a complete project report. Though the model will demonstrate tumor detection capabilities, it must be used alongside professional medical assessments due to the inherent risks of false predictions.
