Multi-Angle Lateral-View Breast Tissue Classification in Thermographic Images Using Deep Reinforcement Learning and Statistical Shape Modelling
- 1Shahed University
Abstract
This study is a multi-stage method for accurately segmenting and classifying breast tissue in thermographic images, with a specific focus on lateral views captured at multiple angles (30{degrees}, 45{degrees}, and 90{degrees}). Our algorithm is a hybrid boundary detection framework that combines scanning, tracking, and a deep reinforcement learning (DRL) algorithm to delineate the external breast tissue boundary precisely. The DRL agent corrects inaccurately detected landmark points and reduces the average localization error to 17.4 pixels. A Statistical Shape Model (SSM) captures shape variability and refines landmark alignment. A geometric arc-based method estimates the posterior breast boundary, which facilitates robust breast tissue isolation across various lateral view angles. After that, we pull texture features using GLCM (Grey Level Co-occurrence Matrix) and feed them through an SVM classifier. The proposed pipeline boosted diagnostic performance - the SVM hit 74.1% accuracy on our segmented images. It is about 15% better than raw originals and 21% above cropped images. The results prove the effectiveness of our multi-angle lateral view analysis combined with reinforcement learning and statistical shape modelling in enhancing breast cancer detection from thermographic imaging.
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BibTeX
@misc{karimi2026multiangle,
title = {{Multi-Angle Lateral-View Breast Tissue Classification in Thermographic Images Using Deep Reinforcement Learning and Statistical Shape Modelling}},
author = {F. Karimi and A. H. Foruzan and Y.-W. Chen},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.20.760338},
url = {https://www.biorxiv.org/content/10.64898/2026.10.20.760338v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Multi-Angle Lateral-View Breast Tissue Classification in Thermographic Images Using Deep Reinforcement Learning and Statistical Shape Modelling
AU - F. Karimi
AU - A. H. Foruzan
AU - Y.-W. Chen
PY - 2026
DA - 2026/10/20
PB - bioRxiv
DO - 10.64898/2026.10.20.760338
UR - https://www.biorxiv.org/content/10.64898/2026.10.20.760338v1
AB - This study is a multi-stage method for accurately segmenting and classifying breast tissue in thermographic images, with a specific focus on lateral views captured at multiple angles (30{degrees}, 45{degrees}, and 90{degrees}). Our algorithm is a hybrid boundary detection framework that combines scanning, tracking, and a deep reinforcement learning (DRL) algorithm to delineate the external breast tissue boundary precisely. The DRL agent corrects inaccurately detected landmark points and reduces the average localization error to 17.4 pixels. A Statistical Shape Model (SSM) captures shape variability and refines landmark alignment. A geometric arc-based method estimates the posterior breast boundary, which facilitates robust breast tissue isolation across various lateral view angles. After that, we pull texture features using GLCM (Grey Level Co-occurrence Matrix) and feed them through an SVM classifier. The proposed pipeline boosted diagnostic performance - the SVM hit 74.1% accuracy on our segmented images. It is about 15% better than raw originals and 21% above cropped images. The results prove the effectiveness of our multi-angle lateral view analysis combined with reinforcement learning and statistical shape modelling in enhancing breast cancer detection from thermographic imaging.
ER -