Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection.
Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep patholog...
| Published in: | BioMed Research International pp. 1 - 13 |
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| Main Authors: | , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
6/28/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157685450&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157685450 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 6/28/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 157685450 157685450 157685450 10.1155/2022/4609625 157685450 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection. aug: au: Bhende, Manisha Thakare, Anuradha Pant, Bhasker Singhal, Piyush Shinde, Swati Saravanan, V. affil: Marathwada Mitra Mandal's Institute of Technology, Pune, India sug: subj: Breast Neoplasms Diagnosis Deep Learning Human Mammography Genetics Algorithms Correlational Studies Predictive Value of Tests ab: Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep pathological information mining is proposed: constructing a sample selection strategy, screening high-quality samples across different mammography image datasets, and dealing with the scarcity of medical image samples from the perspective of data enhancement; mining the pathology contained in limited labeled models from shallow to deep information; and dealing with the shortage of medical image samples from the perspective of feature optimization. The multiview effective region gene optimization (MvERGS) algorithm is designed to refine the original image features, improve the feature discriminate and compress the feature dimension, better match the number of samples, and perform discriminate correlation analysis (DCA) on the advanced new features; in-depth cross-modal correlation between heterogeneous elements, that is, the deep pathological information, can be mined to describe the breast mass lesion area accurately. Based on deep pathological information and traditional classifiers, an efficient breast mass recognition model is trained to complete the classification of mammography images. Experiments show that the key technical indicators of the recognition model, including accuracy and AUC, are better than the mainstream baselines, and the overfitting problem caused by the scarcity of samples is alleviated. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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