Integrated Hierarchical Attention-Convolutional Temporal Network (HACTNet) and Dynamic Hybrid Neuro-Fuzzy Decision Network (DHNFD-Net) for Robotic Automation-Enabled Prediction of Breast Cancer Histological Subtypes and Lymph Node Involvement

The model proposed in this work involves the usage of Hierarchical Attention Convolutional Temporal Network(HACTNet) and Dynamic Hybrid Neuro-Fuzzy Decision Network (DHNFD-Net) for its computational methodologies to automate breast cancer histological subtype and lymph node involvement predictions. Feature extraction and neural networks with fuzzy logic rules are designed to be highly invariant to P300 activity, which leads to highly accurate prognostic and diagnostic rule generation along with robotic automation in an integrated solution. Breast cancer is the leading cause of death worldwide, necessitating better diagnostics and personalised therapy. Build HACTNet and extract spatial-temporal features effectively. Apply DHNFD-Net for interpretable and knew perception-based decision-making. It is necessary to turn to AI as the conventional methods of pesticides are less precise, Less Explainable, and Less Scalable. Integrate these models into a robotic automation framework for real-time diagnostics. The latter includes hierarchical attention, convolutional layers for ACTNet, and neural networks with fuzzy logic for interpretable lymph node predictions of DHNFD-Net. The integrated system automates picture processing, categorization, and decision-making. This system provides 96% classification accuracy, 0.95 interpretability, 95% sensitivity and 65 images/second scalability. Inference latency has been reduced to 110 ms, ensuring real-time applicability. A proposed methodology recommends a uniform, interpretable, and scalable diagnostic framework for breast cancer, opening a wide-field vista apart all over the current methods of obtaining more significant accuracy and efficiency, thus allowing precision, real-time application, and clinical decision-making skills(Graphics-Breast cancer diagnostic framework).