Advancing Early Detection of Breast Cancer: A User-Friendly Convolutional Neural Network Automation System

Abstract

Background: Deep learning models have shown potential in improving cancer diagnosis and treatment. This study aimed to develop a convolutional neural network (CNN) model to predict Invasive Ductal Carcinoma (IDC), a common type of breast cancer. Additionally, a user-friendly interface was designed to facilitate the use of the model by healthcare professionals. Methods: The CNN model was trained and tested using a dataset of high-resolution microscopic images derived from 162 whole-mount slide images of breast cancer specimens. These images were meticulously scanned at 40× magnification using a state-of-the-art digital slide scanner to capture detailed information. Each image was then divided into 277,524 patches of 50 × 50 pixels, resulting in a diverse dataset containing 198,738 IDC-negative and 78,786 IDC-positive patches. Results: The model achieved an accuracy of 98.24% in distinguishing between benign and malignant cases, demonstrating its effectiveness in cancer detection. Conclusions: This study suggests that the developed CNN model has promising potential for clinical applications in breast cancer diagnosis and personalized treatment strategies. Our study further emphasizes the importance of accurate and reliable cancer detection methods for timely diagnosis and treatment. This study establishes a foundation for utilizing deep learning models in future cancer treatment research by demonstrating their effectiveness in analyzing large and complex datasets. This approach opens exciting avenues for further research and potentially improves our understanding of cancer and its treatment.