BRAIN ANEURYSM CLASSIFICATION VIA WHALE OPTIMIZED DENSE NEURAL NETWORK
Journal Title: International Journal of Data Science and Artificial Intelligence - Year 2024, Vol 2, Issue 02
Abstract
A brain aneurysm is caused by faulty blood vessel walls. When a brain aneurysm ruptures or leaks, it can cause bleeding in the brain. It is common for brain aneurysms to not burst but to damage the body and cause symptoms. In this paper, a novel WHO-DNN model (Dense neural network optimized with Whale optimization algorithm) has been proposed to identify the types of aneurysms classes. For this classification, the segmented MRI images are used as input to advance the survival rate of patients. Initially, the segmented MRI images are pre-processed by Adaptive Median Filter (AMF) to remove the noise from the input images. Then, the textural features are extracted to generate the feature sets. The Dense Neural Network (DNN) is utilized to identify and classify the input images to discriminate types of aneurysms classes namely normal, Fusiform aneurysn, and Pseudo aneurysm. Finally, the Whale Optimization Algorithm (WHO) to improve the parameters of the DNN to attain better classification results. The competence of the proposed WHO-DNN model was determined through specific network metrics. The proposed WHO-DNN model attains a total accuracy of 98.69%, which is comparatively better than the existing techniques.
Authors and Affiliations
Ghazanfar Ali Safdar, Xiaochun Cheng
Keywords
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