Early Breast Cancer Detection Using Deep Learning Classification
Résumé
Breast cancer is one of the most prevalent and lethal forms of cancer among women. Early detection and correct analysis play crucial roles in improving patient results and increasing survival rates. However, conventional strategies for the screening and analysis of most breast cancers, including mammography, ultrasound, and biopsy, may be limited by their accuracy and specificity, mainly due to the neglect of fine-lesion cases. This paper describes a system for the early detection of breast cancer, based on a Deep Learning (DL) strategy to enhance the accuracy of breast cancer detection. The proposed system uses a Convolutional Neural Network (CNN) trained on the DDSM database of mammogram images to process and classify suspicious lesions with high precision. The DL model is optimized using advanced techniques, transfer learning, data augmentation, and the ResNet50 model to improve its performance and generalization skills and capabilities. The implementation results demonstrated significant precision (98%), especially in the detection of fine lesions and suspicious microcalcifications.
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