IMPROVING BRAIN CANCER DETECTION THROUGH AI DRIVEN OBJECT RECOGNAISATION

Electronic and communication

The abstract titled "Improving Brain Cancer Detection through AI-Driven Object Recognition" explores Utilizing artificial intelligence (AI) in enhancing The accuracy and efficiency of brain cancer diagnosis. By leveraging advanced object recognition techniques, Systems with artificial intelligence can analyze data from medical imaging to determine and delineate tumor regions, facilitating early detection and precise localization of cancerous tissues. This approach aims to support clinicians in making well- informed choices, which eventually result in improved patient outcomes. Brain cancer diagnosis heavily relies on precise identification and interpretation of medical images. Recent advancements in AI (artificial intelligence) and object recognition techniques offer promising solutions to improve accuracy of diagnosis and efficiency. This study explores the use of AI-driven object recognition in brain cancer detection, leveraging CNNs, or convolutional neural networks, and transfer learning to identify tumors in both CT and MRI scans. Our High accuracy is attained by the suggested framework.in tumor detection, segmentation, and classification, outperforming traditional methods. By integrating AI-driven object recognition into clinical workflows, radiologists can benefit from enhanced diagnostic support, reduced interpretation time, and improved patient outcomes. This research demonstrates the potential of AI in revolutionizing brain cancer diagnosis and treatment

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