Abstract

 Invasive ductal carcinoma (IDC) is the most com- mon type of breast cancer, accounting for approximately 80% of cases. Accurate and early diagnosis of IDC is critical for effective treatment and improved patient survival rates. This study explores the use of convolutional neural networks (CNN) for the classification of IDC in histological breast tissue images, aiming to develop a computer-aided diagnostic (CAD) system that can support pathologists in identifying cancerous tissues. Using a public dataset of 5,547 labeled images, resized to 50x50 pixels to balance computational efficiency and the retention of diagnostically relevant features, we trained a CNN model optimized for binary classification (IDC vs. non-IDC). The preprocessing steps included image normalization and class balancing, with training and validation sets split in an 80:20 ratio. The CNN architecture utilized three convolutional layers with batch normalization and max-pooling, a dense layer with ReLU activation, and a final sigmoid-activated output layer. The model achieved an accuracy of 78%, with pre cision, recall, and F1-scores all at 0.78, and an area under the ROC curve (AUC) of 0.84, indicating effective discrimination between classes. These results suggest that CNN-based models hold promise for aiding in IDC diagnosis, although further research is needed to improve model performance. Future work will focus on exploring advanced architectures, data augmentation, and transfer learning to improve sensitivity and clinical applicability.

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