Enhancing Brain Tumor Diagnosis: A Comprehensive AI Approach Using BTuNet for Classification of MRI Images
Keywords:
brain tumor, MRI images, BTuNet, glioma, meningioma, non-tumor cases, and pituitary, VGG-19, Long short-term memory.Abstract
A brain tumor is an abnormal growth in the brain or spinal canal, classified as benign or malignant. MRI is crucial for diagnosis, especially in distinguishing between conditions like glioma, meningioma, non-tumor cases, and pituitary conditions. Manual interpretation by radiologists, the traditional method, is time-consuming, subjective, and prone to error. AI models are essential for improving brain tumour diagnosis accuracy, consistency, objectivity, and early detection. This leads to better patient outcomes and more efficient healthcare delivery. This research aim to develop an AI based diagnosis model named BTuNet to classify the MRI brain image like glioma, meningioma, non-tumor cases, and pituitary conditions. The research employs a Gaussian filter for preprocessing and utilizes BTuNet for feature extraction and classification. The outcomes show that the proposed BTuNet achieves superior performance in classifying MRI brain images in all the measures employed, especially with an accuracy of 98%, surpassing other state-of-the-art techniques. The research contributes valuable insights, enhancing diagnostic tools and methodologies, with potential benefits for timely medical interventions and improved patient outcomes.
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