Advancing Healthcare: CNN-Based Brain Hemorrhage Detection in Intelligent Environments
Sumit Tanwar, Nipun Choudhary, Vaibhav Chaudhary, Anish Dahiya, Priyanka Kaushik, Rachna Rathore
Abstract
In the realm of medical diagnostics, in time detection of brain hemorrhage is paramount, as the failure to identify & address this condition promptly can result in irreversible brain damage or even fatality. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown remarkable potential in recognizing & classifying various medical disorders based on imaging data. In this research paper, we introduce a CNN-based approach for the identification of the cerebral hemorrhages using computed tomography (CT) images. Our proposed technique encompasses a preprocessing step for isolating the brain region within CT scans, followed by the training of a CNN model to categorize these images into two classes: normal & hemorrhage. To evaluate the effectiveness of our approach, we conducted extensive experiments using a dataset comprising CT images obtained from patients both with & without cerebral hemorrhage. Our results reveal that the proposed CNN-based technique excels in identifying the cerebral hemorrhages & achieving an outstanding overall accuracy of 95.6% & an impressive specificity of 97.8%. This breakthrough in accuracy and specificity holds significant promise for enhancing the diagnostic process. By providing quicker and more accurate identification of this life-threatening condition, our approach has the potential to expedite the initiation of life-saving treatments, ultimately improving patient outcomes in cases of cerebral hemorrhage.