A novel approach of classifying ABO blood group image dataset using deep learning algorithm

Deep learning is important in the medical profession, and it has a wide range of applications, including diagnosis, research, and so on. In imaging technology, classifying the medical images in an automatic way is onerous. In the proposed work, the ABO blood group identification using novel deep learning approach for enhancement of bio medical automation. The ABO blood group data set is developed and classify the blood group automatically using Convolute neural network (CNN) which is capable of extracting and learning features from medical image dataset. As a result, the proposed innovative CNN framework is used in the medical field to classify human blood classes. As a result, our proposed dataset is used to train the model and test the sample in order to identify blood group in the shortest time possible with a 96.7 percent accuracy. The results of the proposed model are compared to those of existing CNN models such as Alex net and Lenet5. The findings show that the proposed method is the most appropriate for classifying human blood groups in medical applications.