Enhancing Breast Cancer Detection Through Optimized Thermal Image Analysis Using PRMS-Net Deep Learning Approach.
Breast cancer has remained one of the most frequent and life-threatening cancers in females globally, putting emphasis on better diagnostics in its early stages to solve the problem of therapy effectiveness and survival. This work enhances the assessment of breast cancer by employing progressive res...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 39; no. 1; pp. 864 - 884 |
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| Autores principales: | , , , , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2026
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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=191694153&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191694153 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: Feb2026 vid: 39 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191694153 191694153 191694153 10.1007/s10278-025-01465-y 191694153 ppf: 864 ppct: 20 formats: tig: atl: Enhancing Breast Cancer Detection Through Optimized Thermal Image Analysis Using PRMS-Net Deep Learning Approach. aug: au: Khan, Mudassir Su'ud, Mazliham Mohd Alam, Muhammad Mansoor Karimullah, Shaik Shaik, Fahimuddin Subhan, Fazli affil: https://ror.org/052kwzs30 Department of Computer Science, College of Computer Science, Applied College Tanumah, King Khalid University, P.O. Box: 960, 61421, Abha, Saudi Arabia sug: subj: Breast Neoplasms Diagnosis Early Detection of Cancer Deep Learning Methods Convolutional Neural Networks Thermography Methods Quality Improvement Human Female Comparative Studies Descriptive Statistics One-Way Analysis of Variance Analysis of Variance Post Hoc Analysis Sensitivity and Specificity Radiologists False Positive Results Image Processing, Computer Assisted Diagnostic Imaging Machine Learning Algorithms Female ab: Breast cancer has remained one of the most frequent and life-threatening cancers in females globally, putting emphasis on better diagnostics in its early stages to solve the problem of therapy effectiveness and survival. This work enhances the assessment of breast cancer by employing progressive residual networks (PRN) and ResNet-50 within the framework of Progressive Residual Multi-Class Support Vector Machine-Net. Built on concepts of deep learning, this creative integration optimizes feature extraction and raises the bar for classification effectiveness, earning an almost perfect 99.63% on our tests. These findings indicate that PRMS-Net can serve as an efficient and reliable diagnostic tool for early breast cancer detection, aiding radiologists in improving diagnostic accuracy and reducing false positives. The separation of the data into different segments is possible to determine the architecture's reliability using the fivefold cross-validation approach. The total variability of precision, recall, and F1 scores clearly depicted in the box plot also endorse the competency of the model for marking proper sensitivity and specificity—highly required for combating false positive and false negative cases in real clinical practice. The evaluation of error distribution strengthens the model's rationale by giving validation of practical application in medical contexts of image processing. The high levels of feature extraction sensitivity together with highly sophisticated classification methods make PRMS-Net a powerful tool that can be used in improving the early detection of breast cancer and subsequent patient prognosis. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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