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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Related citation:N V Shamna,B Aziz Musthafa.Brain Tumor Retrieval in MRI Images with Integration of Optimal Features[J].Journal of Harbin Institute Of Technology(New Series),2024,31(6):71-83.DOI:10.11916/j.issn.1005-9113.2023099.
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Brain Tumor Retrieval in MRI Images with Integration of Optimal Features
Author NameAffiliation
N V Shamna Department of Computer Science and Engineering, PA College of Engineering, Mangaluru 574153, Karnataka, India 
B Aziz Musthafa Department of Computer Science and Engineering, Bearys Institute of Technology, Mangaluru 5754199, Karnataka, India 
Abstract:
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.
Key words:  medical images  brain MRI  machine learning  feature extraction and reduction  content-based image retrieval (CBIR)
DOI：10.11916/j.issn.1005-9113.2023099
Clc Number:TP391.4
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