Cancer Classification Revolution: Employing Advanced Deep CNNs for Multi-Class Detection of Breast Irregularities

early detection plays a pivotal role in reducing breast cancer mortality rates significantly. Detecting breast cancer at an early stage can increase positive outcomes by up to 8%. While radiologists analyze breast images using techniques such as mammograms, X-rays, and MRIs, accurately identifying features like micro calcifications, lumps, and masses remains challenging, leading to high false positives and false negatives. Recent advancements in deep learning and image processing offer hope for more advanced tools in early breast cancer diagnosis. This study focuses on constructing a Deep Convolutional Neural Network (CNN) capable of recognizing and categorizing various breast abnormalities, including lumps, asymmetries, calcifications, and carcinomas. Unlike previous research that primarily differentiated between benign and malignant cancers, this approach allows for more specialized disease treatment. The methodology involves transfer learning using a pre-trained model (ResNet50), fine-tuning it with the available dataset, and developing an enhanced deep learning model emphasizing learning speed. The proposed deep learning model achieved an impressive 88% accuracy in classifying masses, calcifications, carcinomas, and asymmetrical mammograms. This progress holds significant promise for improving the overall accuracy of breast cancer diagnosis and subsequent treatment decisions. The study aligns with ongoing efforts to enhance early detection and treatment, potentially saving lives through more efficient medical procedures.