ArticleJournal of imaging informatics in medicine2025
A Lightweight Method for Breast Cancer Detection Using Thermography Images with Optimized CNN Feature and Efficient Classification.
Article in Journal of imaging informatics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- A Hybrid Multi-CNN Feature Fusion and LASSO Optimization Approach for High-Performance Breast Cancer Classification.Journal of imaging informatics in medicine · 2026Article
- Colormaps Matter: Evaluating Their Impact on CNN-Based Clinical Thermal Imaging.Proceedings of machine learning research · 2026Article
- Artificial Intelligence in IR Thermal Imaging and Sensing for Medical Applications.Sensors (Basel, Switzerland) · 2025Review
- LightweightUNet: Multimodal Deep Learning with GAN-Augmented Imaging Data for Efficient Breast Cancer Detection.Bioengineering (Basel, Switzerland) · 2025Article
- The role of artificial intelligence in enhancing breast cancer screening and diagnosis: A review of current advances.BioImpacts : BI · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (
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Registered trials
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