VRBR-NET: A NOVEL DEEP LEARNING MODEL FOR THERMOGRAPHIC BREAST CANCER DETECTION

Breast cancer remains a leading cause of mortality worldwide, and early, accurate detection is critical for effective treatment and improved patient outcomes. This paper addresses the challenge of advanced breast cancer detection by introducing IVRBR-Net, a novel deep learning model that integrates Inception V4, ResNet-50, and Bidirectional Recurrent Neural Networks (RNNs) to analyze thermographic images. The methodology begins with rigorous preprocessing steps—grayscale conversion, contrast enhancement via Multipurpose Beta Optimized Bi-histogram Equalization (MBOBHE), noise reduction through bilateral filtering, and image refinement using the Affine Projection Algorithm (APA)—to optimize image quality while preserving essential features. Feature extraction is performed using Scale-Invariant Feature Transform (SIFT) and Haralick descriptors, capturing critical statistical properties such as mean, variance, entropy, and skewness. The model is trained and validated on a dataset of 120 high-resolution thermographic images from the Database for Mastology Research (DMR). IVRBR-Net achieves outstanding performance metrics, including an accuracy of 99.82%, specificity of 99.74%, sensitivity of 99.35%, precision of 99.48%, and an F1-score of 99.68%, significantly outperforming existing state-of-the-art models such as MobileNetV2, VGG-16 with attention, and ResNet-50. These results demonstrate the model’s potential as a reliable and precise tool for breast cancer diagnostics, offering a robust framework for future applications in medical image analysis and contributing to improved clinical decision-making