A Lightweight Method for Breast Cancer Detection Using Thermography Images with Optimized CNN Feature and Efficient Classification.
Breast cancer is a prominent cause of death among women worldwide. Infrared thermography, due to its cost-effectiveness and non-ionizing radiation, has emerged as a promising tool for early breast cancer diagnosis. This article presents a hybrid model approach for breast cancer detection using therm...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 3; pp. 1434 - 1452 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=185280513&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 185280513 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2025 vid: 38 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 185280513 185280513 185280513 10.1007/s10278-024-01269-6 185280513 ppf: 1434 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Lightweight Method for Breast Cancer Detection Using Thermography Images with Optimized CNN Feature and Efficient Classification. aug: au: Nguyen Chi, Thanh Le Thi Thu, Hong Doan Quang, Tu Taniar, David affil: Institute of Information Technology, AMST, Hanoi, Vietnam sug: subj: Breast Neoplasms Diagnosis Cancer Screening Methods Thermography Utilization Thermography Methods Thermography Classification Convolutional Neural Networks Women's Health Human Female Chi Square Test Descriptive Statistics Comparative Studies Validity Machine Learning Experimental Studies ROC Curve Female ab: Breast cancer is a prominent cause of death among women worldwide. Infrared thermography, due to its cost-effectiveness and non-ionizing radiation, has emerged as a promising tool for early breast cancer diagnosis. This article presents a hybrid model approach for breast cancer detection using thermography images, designed to process and classify these images into healthy or cancerous categories, thus supporting disease diagnosis. Multiple pre-trained convolutional neural networks are employed for image feature extraction, and feature filter methods are proposed for feature selection, with diverse classifiers utilized for image classification. Evaluating the DRM-IR test set revealed that the combination of ResNet34, Chi-square ( χ 2 ) filter, and SVM classifier demonstrated superior performance, achieving the highest accuracy at 99.62 % . Furthermore, the highest accuracy improvement obtained was 18.3 % when using the SVM classifier and Chi-square filter compared to regular convolutional neural networks. The results confirmed that the proposed method, with its high accuracy and lightweight model, outperforms state-of-the-art breast cancer detection from thermography image methods, making it a good choice for computer-aided diagnosis. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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