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Utilizing deep learning for the real-time detection of breast cancer through thermography

The prevention of breast cancer at an early stage is crucial for saving lives and reducing costs. Breast thermography, a complementary diagnostic technique, has shown promise in detecting breast tumors early. This research proposes a framework that leverages real-time thermography video streaming an...

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Main Authors: Al Husaini, Mohammed Abdulla Salim, Habaebi, Mohamed Hadi, Islam, Md. Rafiqul
Format: Proceeding Paper
Language:English
English
Published: 2023
Subjects:
TK5101 Telecommunication. Including telegraphy, radio, radar, television
Online Access:http://irep.iium.edu.my/107819/7/107819_Utilizing%20deep%20learning%20for%20the%20real-time%20detection.pdf
http://irep.iium.edu.my/107819/8/107819_Utilizing%20deep%20learning%20for%20the%20real-time%20detection_Scopus.pdf
http://irep.iium.edu.my/107819/
https://ieeexplore.ieee.org/document/10246061
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spelling my.iium.irep.1078192024-02-27T10:01:57Z http://irep.iium.edu.my/107819/ Utilizing deep learning for the real-time detection of breast cancer through thermography Al Husaini, Mohammed Abdulla Salim Habaebi, Mohamed Hadi Islam, Md. Rafiqul TK5101 Telecommunication. Including telegraphy, radio, radar, television The prevention of breast cancer at an early stage is crucial for saving lives and reducing costs. Breast thermography, a complementary diagnostic technique, has shown promise in detecting breast tumors early. This research proposes a framework that leverages real-time thermography video streaming and deep learning models for the early detection of breast cancer. The framework, implemented in MATLAB 2019 on a standard Desktop with a thermal camera, captures high-quality real-time video streams, which are then used as input for classifying normal and abnormal breasts using deep convolutional neural network models, specifically Inception v3, Inception v4, and a modified Inception Mv4. The results demonstrate that the Inception Mv4 model, combined with real-time video streaming, effectively detects even the slightest temperature contrasts in breast tissue by generating a sequence of thermal images from different angles. The contrast is further improved by applying cooling gel to the breast area, resulting in an efficient image acquisition process and accurate detection. Additionally, the study reveals that a mere 0.1% increase in the temperature of the tumor surface area leads to an average improvement of 7% in detection and classification accuracy. 2023-08-15 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/107819/7/107819_Utilizing%20deep%20learning%20for%20the%20real-time%20detection.pdf application/pdf en http://irep.iium.edu.my/107819/8/107819_Utilizing%20deep%20learning%20for%20the%20real-time%20detection_Scopus.pdf Al Husaini, Mohammed Abdulla Salim and Habaebi, Mohamed Hadi and Islam, Md. Rafiqul (2023) Utilizing deep learning for the real-time detection of breast cancer through thermography. In: 9th International Conference on Computer and Communication Engineering (ICCCE 2023), 5-16 August 2023, Kuala Lumpur, Malaysia. https://ieeexplore.ieee.org/document/10246061 doi:10.1109/ICCCE58854.2023.10246061
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic TK5101 Telecommunication. Including telegraphy, radio, radar, television
spellingShingle TK5101 Telecommunication. Including telegraphy, radio, radar, television
Al Husaini, Mohammed Abdulla Salim
Habaebi, Mohamed Hadi
Islam, Md. Rafiqul
Utilizing deep learning for the real-time detection of breast cancer through thermography
description The prevention of breast cancer at an early stage is crucial for saving lives and reducing costs. Breast thermography, a complementary diagnostic technique, has shown promise in detecting breast tumors early. This research proposes a framework that leverages real-time thermography video streaming and deep learning models for the early detection of breast cancer. The framework, implemented in MATLAB 2019 on a standard Desktop with a thermal camera, captures high-quality real-time video streams, which are then used as input for classifying normal and abnormal breasts using deep convolutional neural network models, specifically Inception v3, Inception v4, and a modified Inception Mv4. The results demonstrate that the Inception Mv4 model, combined with real-time video streaming, effectively detects even the slightest temperature contrasts in breast tissue by generating a sequence of thermal images from different angles. The contrast is further improved by applying cooling gel to the breast area, resulting in an efficient image acquisition process and accurate detection. Additionally, the study reveals that a mere 0.1% increase in the temperature of the tumor surface area leads to an average improvement of 7% in detection and classification accuracy.
format Proceeding Paper
author Al Husaini, Mohammed Abdulla Salim
Habaebi, Mohamed Hadi
Islam, Md. Rafiqul
author_facet Al Husaini, Mohammed Abdulla Salim
Habaebi, Mohamed Hadi
Islam, Md. Rafiqul
author_sort Al Husaini, Mohammed Abdulla Salim
title Utilizing deep learning for the real-time detection of breast cancer through thermography
title_short Utilizing deep learning for the real-time detection of breast cancer through thermography
title_full Utilizing deep learning for the real-time detection of breast cancer through thermography
title_fullStr Utilizing deep learning for the real-time detection of breast cancer through thermography
title_full_unstemmed Utilizing deep learning for the real-time detection of breast cancer through thermography
title_sort utilizing deep learning for the real-time detection of breast cancer through thermography
publishDate 2023
url http://irep.iium.edu.my/107819/7/107819_Utilizing%20deep%20learning%20for%20the%20real-time%20detection.pdf
http://irep.iium.edu.my/107819/8/107819_Utilizing%20deep%20learning%20for%20the%20real-time%20detection_Scopus.pdf
http://irep.iium.edu.my/107819/
https://ieeexplore.ieee.org/document/10246061
_version_ 1792146412157272064
score 13.211869

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