Blood Group Detection Using Vision Transformer

The “Blood Group Detection using Vision Transformer” project focuses on creating a novel system based on thumb impressions to detect blood groups. The system increases accuracy and efficiency in detecting blood groups by utilizing deep learning methods, i.e., Convolutional Neural network (CNN) variants like ResNet integrated with Recurrent Neural Network (RNN) and MobileNet and Vision Transformer. The user Interface has made this project more interactive by using Flask in the backend. In the evaluation of model performance across different architectures, the results reveal distinct levels of accuracy. The MobileNet achieved an accuracy of 75.04% on the training set and 77.56% on the validation set, demonstrating a solid performance with a loss of 0.6431 and 0.5700, respectively. The ResNet combined with an RNN exhibited lower accuracy, achieving 61.45% on the training data and 75.57% on validation, with corresponding losses of 1.0035 and 0.6754, The Vision Transformer outperformed all models, reaching an impressive accuracy of 97.84% on the training set and 92.52% on validation, accompanied by a loss of 0.0673 and 0.2618.