Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/519575
Title: Advanced soft computing approaches for content based mammography image retrieval
Researcher: Selvi,A
Guide(s): Thilagamani,S
Keywords: Cancer
Content-Based Image Retrieval
Information And Communication Engineering
Medical science
University: Anna University
Completed Date: 2023
Abstract: iii newlineABSTRACT newlineMedical science has successfully employed digital image processing to newlineimprove productivity in a variety of areas, including the study of anatomical newlinestructure, diagnosis, surgical planning, treatment planning, research, newlinecomputer-assisted surgery, and many more. Recent years have seen a newlinesignificant increased interest in medical research and applications for the newlinedeveloping fields of image processing and artificial intelligence. Cancer is a newlinechronic non communicable disease that affects people of all ages, social newlineclasses, and races indistinctly. Receiving the diagnosis of this illness is newlinevirtually always distressing for the patient due to its unpredictable nature. In newlinethis list, breast cancer, particularly in females, holds a prominent place. newlineHowever, there is a very good chance of treatment if it is discovered quickly. newlineIn this way, the development of digital technology has accelerated in order to newlineaid in the early detection of sickness. Diagnostic imaging is frequently used in newlinethe examination of breast cancer from a clinical standpoint. Mammography is newlineone of the most popular tests and is regarded as the primary one for the early newlinediagnosis of this form of cancer. Images are thought to play a big part in the newlinemammography-based detection of breast cancer. Content-Based Image newlineRetrieval (CBIR) contributes by classifying the mammography image under newlinequery and retrieving comparable mammography images from the database. newlineNumerous studies have been conducted, and the results were frequently newlineunreliable when primitive features were extracted for similarity matching. newlineTherefore, the research presented the optimal classification of crow search newlineoptimization algorithms with a deep belief network to increase the detecting newlinesimilarity of images and reduce the average computing time. newline newline
Pagination: xii,157p.
URI: http://hdl.handle.net/10603/519575
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File22.73 kBAdobe PDFView/Open
02_prelim_pages.pdf2.34 MBAdobe PDFView/Open
03_content.pdf434.58 kBAdobe PDFView/Open
04_abstract.pdf11.22 kBAdobe PDFView/Open
05_chapter 1.pdf298.33 kBAdobe PDFView/Open
06_chapter 2.pdf296.38 kBAdobe PDFView/Open
07_chapter 3.pdf885.18 kBAdobe PDFView/Open
08_chapter 4.pdf916.36 kBAdobe PDFView/Open
09_chapter 5.pdf611.8 kBAdobe PDFView/Open
10_annexures.pdf262.83 kBAdobe PDFView/Open
80_recommendation.pdf206.74 kBAdobe PDFView/Open
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