ArticleMultimedia tools and applications2022
Screening of breast cancer from thermogram images by edge detection aided deep transfer learning model.
Article in Multimedia tools and applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Enhancing Breast Cancer Detection Through Optimized Thermal Image Analysis Using PRMS-Net Deep Learning Approach.Journal of imaging informatics in medicine · 2026Article
- JUHCCR-v1: a database for hand-drawn electrical and electronics circuit component recognition.Scientific reports · 2025Article
- Dual-Tree Complex Wavelet Pooling and Attention-Based Modified U-Net Architecture for Automated Breast Thermogram Segmentation and Classification.Journal of imaging informatics in medicine · 2025Article
- Segmentation for mammography classification utilizing deep convolutional neural network.BMC medical imaging · 2024Article
- Chimp Optimization Algorithm Influenced Type-2 Intuitionistic Fuzzy C-Means Clustering-Based Breast Cancer Detection System.Cancers · 2023Article
- Article
- Review
- Detecting bone lesions in the emergency room with medical infrared thermography.Biomedical engineering online · 2022Article
- Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Deep Learning-Based Analysis of Mammographic Images for Breast Cancer Detection Using Transfer Learning.Healthcare technology lettersArticle
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Authors and funding
4 authors.
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Abstract
Breast cancer, the most common invasive cancer, causes deaths of thousands of women in the world every year. Early detection of the same is a remedy to lessen the death rate. Hence, screening of breast cancer in its early stage is utmost required. However, in the developing nations not many can afford the screening and detection procedures owing to its cost. Hence, an effective and less expensive way of detecting breast cancer is performed using thermography which, unlike other methods, can be used on women of various ages. To this end, we propose a computer aided breast cancer detection system that accepts thermal breast images to detect the same. Here, we use the pre-trained DenseNet121 model as a feature extractor to build a classifier for the said purpose. Before extracting features, we work on the original thermal breast images to get outputs using two edge detectors - Prewitt and Roberts. These two edge-maps along with the original image make the input to the DenseNet121 model as a 3-channel image. The thermal breast image dataset namely, Database for Mastology Research (DMR-IR) is used to evaluate performance of our model. We achieve the highest classification accuracy of 98.80% on the said database, which outperforms many state-of-the-art methods, thereby confirming the superiority of the proposed model. Source code of this work is available here: https://github.com/subro608/thermogram_breast_cancer.
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