Open Access

ARTICLE

Advancing Breast Cancer Diagnosis: The Development and Validation of the HERA-Net Model for Thermographic Analysis

S. Ramacharan^1,*, Martin Margala¹, Amjan Shaik², Prasun Chakrabarti³, Tulika Chakrabarti⁴

1 School of Computing and Informatics, University of Louisiana, Lafayette, LA 70504, USA
2 Computer Science and Engineering, St. Peter’s Engineering College, Hyderabad, 500100, India
3 Director Research and Dean International Affairs, Sir Padampat Singhania University, Udaipur, 313601, Rajasthan, India
4 Department of Basic Sciences, Sir Padampat Singhania University, Udaipur, 313601, Rajasthan, India

* Corresponding Author: S. Ramacharan. Email:

Computers, Materials & Continua 2024, 81(3), 3731-3760. https://doi.org/10.32604/cmc.2024.058488

Received 13 September 2024; Accepted 19 November 2024; Issue published 19 December 2024

Abstract

Breast cancer remains a significant global health concern, with early detection being crucial for effective treatment and improved survival rates. This study introduces HERA-Net (Hybrid Extraction and Recognition Architecture), an advanced hybrid model designed to enhance the diagnostic accuracy of breast cancer detection by leveraging both thermographic and ultrasound imaging modalities. The HERA-Net model integrates powerful deep learning architectures, including VGG19, U-Net, GRU (Gated Recurrent Units), and ResNet-50, to capture multi-dimensional features that support robust image segmentation, feature extraction, and temporal analysis. For thermographic imaging, a comprehensive dataset of 3534 infrared (IR) images from the DMR (Database for Mastology Research) was utilized, with images captured by the high-resolution FLIR SC-620 camera. This dataset was partitioned with 70% of images allocated to training, 15% to validation, and 15% to testing, ensuring a balanced approach for model development and evaluation. To prepare the images, preprocessing steps included resizing, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for enhanced contrast, bilateral filtering for noise reduction, and Non-Local Means (NLMS) filtering to refine structural details. Statistical metrics such as mean, variance, standard deviation, entropy, kurtosis, and skewness were extracted to provide a detailed analysis of thermal distribution across samples. Similarly, the ultrasound dataset was processed to extract detailed anatomical features relevant to breast cancer diagnosis. Preprocessing involved grayscale conversion, bilateral filtering, and Multipurpose Beta Optimized Bihistogram Equalization (MBOBHE) for contrast enhancement, followed by segmentation using Geodesic Active Contours. The ultrasound and thermographic datasets were subsequently fed into HERA-Net, where VGG19 and U-Net were applied for feature extraction and segmentation, GRU for temporal pattern recognition, and ResNet-50 for classification. The performance assessment of HERA-Net on both imaging modalities demonstrated a high degree of diagnostic accuracy, with the proposed model achieving an overall accuracy of 99.86% in breast cancer detection, surpassing other models such as VGG16 (99.80%) and Inception V3 (99.64%). In terms of sensitivity, HERA-Net reached a flawless 100%, indicating its ability to correctly identify all positive cases, while maintaining a specificity of 99.81%, significantly reducing the likelihood of false positives. The model’s robustness was further illustrated through cross-entropy loss convergence and ROC (Receiver Operating Characteristic) curves, with the combined ROC curve showing consistent discrimination ability across training, validation, and testing phases. Overall, the HERA-Net model’s integration of thermographic and ultrasound imaging, combined with advanced deep learning techniques, showcases a powerful approach to breast cancer detection, achieving unprecedented accuracy and sensitivity.

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