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.