A Dense-Layered Deep Neural Model-Based Classification of Brain Hemorrhages Using Head Computer Tomography Images

Artificial Intelligence (AI) refers to the ability to learn, remember, predict, and make an optimal judgment based on Computer-assisted Design (CAD) Systems. Traditional CAD algorithms and methods on head CT scans focused on the automatic recognition, segmentation, and classification of the abnormalities. But, these approaches were encountered with several limitations like (i) smaller dataset size, (ii) negative Transfer Learning, and (iii) improper localization. This paper proposed the new dense-layered deep neural model to classify brain hemorrhages using head CT scans. The proposed model is the ten-layered network having dense blocks with skip connections. It uses the cross-chained connection between dense blocks to minimize gradient loss while training. Later, the last layer of the model is extended with Grad-Cam for localization of the affected cell regions. The model performance is evaluated on a dataset of head CT scans of size 427.25GB. The dataset is partitioned into 752,800 images in the training set and 121,232 images in the testing set. The experimentation results achieved an accuracy of 98.32% with a mean logarithmic loss of 0.06487. The average classification accuracy of the proposed model on multiple-class hemorrhages is 98.27%. The experimentation results are found satisfactory having the best AUC-ROC accuracy of 98.32%. The comparative analysis of the model with other traditional deep neural networks proves the efficacy of the model in predicting results. Also, in comparison with other methods, the gained results are found satisfactory with an increase in the accuracy of 1.3%.