Robotic Automation-Driven Breast Cancer Prediction Integrating Histological Subtypes and Lymph Node Awareness Using AENF-EL and VEGN-PTD

Correct classification of histological subtypes and assessment of lymph node status is crucial in the diagnosis of breast cancer. Human inefficiency, non-real-time flexibility, and heterogeneity in data are a few limitations of conventional approaches. Automation and machine learning can be possible aids to enhance efficiency and accuracy in diagnosis. The goal is to develop an automated machine learning-based high-precision, scalable breast cancer predictive model that enhances diagnostic efficiency as well as accuracy. This research proposes a robust model that integrates data enrichment using GAN-based techniques, VEGN-PTD for the analysis of lymph nodes, AENF-EL for the classification of histology, and RPA-based preprocessing. All these methods ensure higher accuracy in feature extraction, data augmentation, and classification. The model reduced diagnostic time to 0.75 seconds and yielded 94.1 % accuracy, 92.5% sensitivity, and 93.8% specificity. With the provision of scalable, accurate, and tailored predictions, this solution significantly enhances breast cancer diagnosis.