BREAST CANCER DETECTION USING DEEP LEARNING ON BIOMEDICAL MAMMOGRAM IMAGES

Abstract

Millions of women worldwide are affected by breast cancer, which is a serious global health issue. The likelihood of successful therapy and the prognosis both greatly benefit from early identification. The most popular screening method for breast cancer, mammography, produces precise biological images that can help with the early detection of malignancies. However, it is still difficult to correctly interpret mammography pictures, which frequently results in false positives or negatives. This study attempts to create a biological mammogram based deep learning system for breast cancer diagnosis. Convolutional neural networks (CNNs) are used to automatically identify and analyse mammogram pictures in the proposed system, enabling radiologists to make quicker and more accurate diagnoses. To ensure the best performance during the training phase, these photos underwent preprocessing to reduce noise and enhance characteristics. The deep learning model used is a cutting-edge CNN architecture that was pretrained on a sizable dataset to fully utilise its learned representations. The deep learning model underwent thorough training, validation, and fine-tuning procedures to ensure robustness and generalizability. A variety of data augmentation methods, including rotation, scaling, and flipping, was used to enlarge and diversify the dataset during training. To further increase the model's accuracy, transfer learning was used to utilize knowledge from other similar tasks. Using a variety of criteria, such as sensitivity, specificity, accuracy, and F1 score, and the performance of the created breast cancer detection system was carefully assessed. The results showed a substantial increase in accuracy when compared to traditional mammography analysis methods. The method demonstrated impressive specificity in reducing false positives and sensitivity in identifying actual positive situations.