Infrared Breast Image Segmentation Using Deep Neural Networks on Thermographic Images

Breast cancer is one of the most common and lethal types of cancer worldwide, with millions of new cases diagnosed each year. Early detection is pivotal in improving patient outcomes and significantly increases the chances of successful treatment. While traditional detection methods such as mammogra...

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Bibliographic Details
Published in:Proceedings / IEEE International Symposium on Computer-Based Medical Systems pp. 113 - 118
Main Authors: Da Silva e Souza Pinto, Tiago, Melo, Renata dos Santos, Silva, Paulo Vitor Costa, Backes, Andre Ricardo, Fernandes, Henrique Coelho
Format: Conference Proceeding
Language:English
Published: IEEE 18.06.2025
Subjects:
Accuracy
Breast cancer
Breast tissue
CNN
Computational modeling
Convolutional neural networks
Hair
Image segmentation
infrared image
Infrared imaging
Neck
Noise
region of interest
segmentation
ISSN:2372-9198
Online Access:Get full text
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Summary:Breast cancer is one of the most common and lethal types of cancer worldwide, with millions of new cases diagnosed each year. Early detection is pivotal in improving patient outcomes and significantly increases the chances of successful treatment. While traditional detection methods such as mammography are effective, they can be invasive, costly, painful, and less applicable for younger women with denser breast tissue. In this context, infrared thermography emerges as a promising, non-invasive technique for breast cancer detection. However, analyzing these images presents challenges due to noise and irrelevant information that can interfere with accurate diagnosis. In this work, we propose a method for segmenting infrared breast images using the DeepLabV3+ Convolutional Neural Network (CNN). Our approach leverages the power of deep learning to precisely delineate breast regions, enabling more accurate feature extraction for subsequent classification tasks. Results achieved an average accuracy of 98.69%, an Intersection over Union (IoU) of 97.18%, and a precision of 98.48%, demonstrating a clear improvement over previous approaches, particularly in terms of segmentation quality, making our method a robust tool for enhancing automated breast cancer detection.
ISSN:2372-9198
DOI:10.1109/CBMS65348.2025.00032