Published Paper Details:

This project applies deep transfer learning to understand facial features and ascertain certain diseases including beta-thalassemia, hyperthyroidism, Down syndrome and leprosy. System incorporates VGG16, ALEXNET and Kernel SVM as advanced models and the Disease Specific Face dataset for training and testing performance evaluations. DLIB along with GABOR is used for the preprocessing to extract features and for facial alignment respectively. Of all the models, VGG16 and LSTM had performed best with accuracy of about 99%. The system also uses other performance metrics including accuracy, precision, recall and F-score to confirm the importance of working systems within the provided scope. It can be concluded based on the results of the project that the combination of transfer learning and feature extraction improves the medical diagnosis process and provides an effective overall scheme of facial recognition-based disease detection system.