# A CNN-based methodology for breast cancer diagnosis using thermal images

arXiv: https://arxiv.org/abs/1910.13757 · HTML: https://mlsanity.com/paper/1910.13757/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures](https://mlsanity.com/paper/2305.19937/) (2023)
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- [Towards Human-AI Collaboration System for the Detection of Invasive Ductal Carcinoma in Histopathology Images](https://mlsanity.com/paper/2508.07875/) (2025)
- [Multi-channel deep convolutional neural networks for multi-classifying thyroid disease](https://mlsanity.com/paper/2203.03627/) (2022)
- [Incision trajectory tracing for electrosurgical navigation by CNN-based knife contacting frames extraction method](https://mlsanity.com/paper/2608.14749/) (2026)
- [Explainable Multimodal Deep Learning Integrating Imaging and Clinical Data for Oral Potentially Malignant Disorder Detection](https://mlsanity.com/paper/2609.04512/) (2026)

## Abstract (arXiv)

Micro Abstract: A recent study from GLOBOCAN disclosed that during 2018 two million women worldwide had been diagnosed from breast cancer. This study presents a computer-aided diagnosis system based on convolutional neural networks as an alternative diagnosis methodology for breast cancer diagnosis with thermal images. Experimental results showed that lower false-positives and false-negatives classification rates are obtained when data pre-processing and data augmentation techniques are implemented in these thermal images. Background: There are many types of breast cancer screening techniques such as, mammography, magnetic resonance imaging, ultrasound and blood sample tests, which require either, expensive devices or personal qualified. Currently, some countries still lack access to these main screening techniques due to economic, social or cultural issues. The objective of this study is to demonstrate that computer-aided diagnosis(CAD) systems based on convolutional neural networks (CNN) are faster, reliable and robust than other techniques. Methods: We performed a study of the influence of data pre-processing, data augmentation and database size versus a proposed set of CNN models. Furthermore, we developed a CNN hyper-parameters fine-tuning optimization algorithm using a tree parzen estimator. Results: Among the 57 patients database, our CNN models obtained a higher accuracy (92%) and F1-score (92%) that outperforms several state-of-the-art architectures such as ResNet50, SeResNet50 and Inception. Also, we demonstrated that a CNN model that implements data-augmentation techniques reach identical performance metrics in comparison with a CNN that uses a database up to 50% bigger. Conclusion: This study highlights the benefits of data augmentation and CNNs in thermal breast images. Also, it measures the influence of the database size in the performance of CNNs.