DEVELOPMENT WEB BASED SYSTEM TO RECOMMEND ARTIFICIAL INTELLIGENCE METHODS AND EVALUATION MODELS FOR CANCER DIAGNOSIS AND PROGNOSIS

Abstract

Breast cancer is a common, potentially fatal illness that affects millions of women worldwide. The key to successful therapy and better patient outcomes is an early and precise diagnosis. Deep learning techniques, in particular convolutional neural networks (CNNs), have demonstrated incredible potential in medical image processing in recent years, with the ability to diagnose and prognosticate breast cancer among other conditions. This study focuses on using CNNs to analyze mammography pictures and improve breast cancer detection and prediction. The study used a heterogeneous dataset that included both benign and malignant mammograms from various sources. To detect breast cancer, we improved a pre-trained CNN architecture by concentrating on spotting minute patterns and anomalies that could be signs of cancer. The results showed a significant increase in diagnostic accuracy, with the CNN outperforming conventional techniques with a sensitivity of 92% and a specificity of 89%. To assess the CNN's predictive power, we observed a group of patients who had received a breast cancer diagnosis over an extended period. The model exhibited a notable level of precision in forecasting results, with an area under the receiver operating characteristic (ROC) curve of 0.94. This implies that CNN can accurately predict how breast cancer will progress and help create individualized treatment regimens for patients. The evaluation metrics comprised the following: accuracy, precision, recall, and F1-score. CNN demonstrated a remarkable range of score evaluation amongst the metrics, highlighting its capacity to achieve a balance between detecting positive cases and reducing false positives. In summary, the use of CNNs for the diagnosis and prognostication of breast cancer is a revolutionary development in the realm of medical imaging. Empirical evidence suggests that deep learning can enhance diagnostic precision and forecast patient results. The study demonstrates that the model may detect small irregularities associated with breast cancer through tangible numerical data. These findings facilitate the incorporation of CNNs into clinical practice, providing a valuable instrument for radiologists and oncologists in their endeavor to achieve more precise and individualized breast cancer care.