Brain Tumor Retrieval in MRI Images with Integration of Optimal Features  

作　　者：N V Shamna B Aziz Musthafa 

机构地区：[1]Department of Computer Science and Engineering,PA College of Engineering,Mangaluru 574153,Karnataka,India [2]Department of Computer Science and Engineering,Bearys Institute of Technology,Mangaluru 5754199,Karnataka,India

出　　处：《Journal of Harbin Institute of Technology(New Series)》2024年第6期71-83,共13页哈尔滨工业大学学报（英文版）

摘　　要：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.

关 键 词：medical images brain MRI machine learning feature extraction and reduction content-based image retrieval(CBIR) 

分 类 号：R445.2[医药卫生—影像医学与核医学] R739.41[医药卫生—诊断学]