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

Computers, Materials & Continua 2024年第12期81卷 3731-3760页

作者： S.Ramacharan martin margala Amjan Shaik Prasun Chakrabarti Tulika Chakrabarti School of Computing and Informatics University of LouisianaLafayetteLA 70504USA Computer Science and Engineering St.Peter’s Engineering CollegeHyderabad500100India Director Research and Dean International Affairs Sir Padampat Singhania UniversityUdaipur313601RajasthanIndia Department of Basic Sciences Sir Padampat Singhania UniversityUdaipur313601RajasthanIndia

Breast cancer remains a significant global health concern,with early detection being crucial for effective treatment and improved survival *** study introduces HERA-Net(Hybrid Extraction and Recognition Architec-ture),an advanced hybrid model designed to enhance the diagnostic accuracy of breast cancer detection by leveraging both thermographic and ultrasound imaging *** 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 *** 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 *** 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 *** 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 *** metrics such as mean,variance,standard deviation,entropy,kurtosis,and skewness were extracted to provide a detailed analysis of thermal distribution across ***,the ultrasound dataset was processed to extract detailed anatomical features relevant to breast cancer *** involved grayscale conversion,bilateral filtering,and Multipurpose Beta Optimized Bihistogram Equalization(MBOBHE)for contrast enhancement,followed by segmentation using Geodesic Active *** 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 *** performance assessme

关键词： Cancer breast cancer detection sensitivity deep learning accuracy

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