Unveiling Complex Morphological Patterns in Mammogram Images for Early-Stage Breast Cancer Detection with Siamese Watershed Graph Convolution Networks
DOI:
https://doi.org/10.52783/jns.v14.3122Keywords:
Early-stage breast cancer detection, Mammogram images, Siamese Watershed Graph Convolutional Network (SW-GCN), Classifier performance, Morphological patterns, Deep learning, Diagnostic accuracyAbstract
Breast cancer is the second leading cause of death among women, characterized by the uncontrolled growth of cells. Early detection is crucial for effective treatment, particularly in differentiating between benign and malignant conditions using image processing techniques. X-ray mammography is the most reliable method for early detection, but the resulting images are often complex, making it difficult to accurately extract quantitative and meaningful features from overlapping nuclear morphologies. Advancements in medical imaging are therefore essential. In this study, we present a novel approach using Siamese Watershed Graph Convolutional Networks (SWGCN) to automatically classify abnormalities in mammograms with higher accuracy. The Watershed Graph Convolutional Network technique effectively separates dissimilar nuclei and determines object borders based on gradient and intensity values, even with complex, overlapping features. The Siamese network distinguishes between benign and malignant tumors with improved accuracy by using pairs of nuclei images as inputs and assessing whether the pairs belong to the same class (benign or malignant) based on features extracted by the GCN. Our proposed method demonstrates superior feature extraction capabilities. The SWGCN system achieved high performance, with an F1-score of 90%, recall of 99.2%, accuracy of 97.15%, and precision of 90%. This research represents a significant advancement in image processing methods for healthcare, emphasizing their potential to improve early-stage breast cancer detection.
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