MATLAB-based CNN model with GUI for detecting blood clotting disorders.
Focused on early detection using thermal imaging datasets and an interactive medical interface.
This project developed a Computer-Aided Detection (CAD) system for identifying potential blood clotting disorders. It was implemented in MATLAB, combining deep learning with a graphical user interface (GUI) to allow clinicians to upload thermal or diagnostic images, process them through a CNN, and receive a prediction in real time.
The CNN achieved high classification accuracy on the testing dataset, successfully distinguishing clot-affected images from healthy ones. The GUI integration enabled smooth operation, providing clinicians a non-technical way to run predictions. This made the model usable outside of traditional machine learning environments.
One major challenge was working with thermal medical datasets, which were relatively small and imbalanced. I used augmentation and careful normalization to expand data diversity. MATLAB’s limited real-time GPU support was addressed by optimizing network size and using efficient layer pruning, allowing faster inference for clinical use cases.