An Intracranial Brain Hemorrhage’s Identification and Classification on CT Imaging using Fuzzy Deep Learning

Therapists play a crucial role in a patient’s timely and accurate diagnosis of blood vessels within the skull or brain tissue rupture, which is essential for achieving the best outcomes. This paper discusses the efficacy of computed tomography imaging in the recognition and classification of different intracranial brain hemorrhage subtypes. We present a novel approach using the concept of fuzzy deep learning with ResNet50 for computed tomography image analysis, which has improved the accuracy of classification. This approach has efficiently identified and classified the subtypes of intracranial brain hemorrhage, which include subdural, epidural, intraventricular, intraparenchymal, and subarachnoid hemorrhage. The fuzzy deep learning system enhances the degree of fuzzy logic in the classification process within the cascading model and improves the interpretability of the classifier. The results show that near-perfect accuracy is achieved when the cascading model is utilized. Additionally, the typical computed tomography appearance of each intracranial brain hemorrhage subtype shows how our model identified unique diagnostic features different from those of previous attribute-based models. This fusion of computed tomography scanning with state-of-the-art deep learning illustrates the future of artificial intelligence recommender systems in successfully diagnosing and/or treating strokes. Our study emphasizes the important role that computed tomography imaging plays when combined with deep fuzzy learning techniques in the management of stroke diseases.