SURVEY ON BREAST CANCER DETECTION USING CNN ALGORITHM

Computer Science & Engineering

Breast cancer continues to be one of the leading causes of female death globally. Proper and early disease diagnosis is critical to the maximization of the survival rate of the patients. This research proposes an artificial intelligence-based system for the early detection and classification of breast cancer using deep learning models—specifically Convolutional Neural Networks (CNNs)—for the analysis of mammographic images. The proposed system seeks to detect and classify microcalcifications, which are critical signs of breast cancer, into three precise classes: normal, benign, and malignant.The architecture adheres to a pipeline-based system of image preprocessing, feature extraction, and classification. This rigorous framework improves detection precision, significantly reducing false positives. Additionally, for supplementary analysis via image, the system integrates a symptom-based cancer staging estimation module. The question-and-answer interface of this feature estimates cancer staging stages (0 to 4) to serve as a complementary diagnostic tool for non-image-based cases.The integration of imaging analysis with staging prediction greatly enhances the accuracy of the diagnostic tool without reducing the workload that the radiologists and medical professionals have to handle. By enabling faster and more accurate diagnostic results, the system seeks to improve clinical outcomes, particularly in low-resource environments where expert radiologists may not be present. This research demonstrates the transformational capability of artificial intelligence technology in the medical imaging industry with a valuable and scalable solution to identify breast cancer earlier. The results indicate that the diagnostic devices with the assistance of AI have the potential to serve as integral support systems in contemporary healthcare by facilitating improved early intervention planning and improved treatment.

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