首页|Brain Tumor Retrieval in MRI Images with Integration of Optimal Features
Brain Tumor Retrieval in MRI Images with Integration of Optimal Features
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This paper presents an approach to improve medical image retrieval,particularly for brain tumors,by addressing the gap between low-level visual and high-level perceived contents in MRI,X-ray,and CT scans.Traditional methods based on color,shape,or texture are less effective.The proposed solution uses machine learning to handle high-dimensional image features,reducing computational complexity and mitigating issues caused by artifacts or noise.It employs a genetic algorithm for feature reduction and a hybrid residual UNet(HResUNet)model for Region-of-Interest(ROI)segmentation and classification,with enhanced image preprocessing.The study examines various loss functions,finding that a hybrid loss function yields superior results,and the GA-HResUNet model outperforms the HResUNet.Comparative analysis with state-of-the-art models shows a 4％improvement in retrieval accuracy.
medical imagesbrain MRImachine learningfeature extraction and reductioncontent-based image retrieval(CBIR)
N V Shamna、B Aziz Musthafa
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Department of Computer Science and Engineering,PA College of Engineering,Mangaluru 574153,Karnataka,India
Department of Computer Science and Engineering,Beatys Institute of Technology,Mangaluru 5754199,Karnataka,India
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