A deep learning–based multi-level approach for automatic detection of suspicious regions in breast cancer histopathological images
Breast cancer is one of the most prevalent types of cancer, with high mortality rates all over the world. The disease is linked to uncontrolled growth of breast tissue, resulting in benign (not likely to spread) or malignant (able to spread) tumors. Malignant tumors are particularly dangerous due to their ability to spread. This study analyzes breast cancer histology images using deep learning methods to automatically detect and classify histology as benign or malignant. It used advanced models such as convolutional neural networks (CNNs) and long short-term memory (LSTM) models to account for the spatial and sequential aspects of the images. While CNNs detect textures and patterns that indicate malignancy, LSTM models effectively capture temporal and feature dependencies, making the representation complete. Our hybrid model (CNN combined with LSTM) outperformed single-scale approaches in accuracy and classification diversity. The hybrid model was independently tested on networks of Gated Recurrent Unit, Visual Geometry Group 16, and Residual Network 50. It achieved unprecedented scores of up to 96.21% accuracy, 95.21% sensitivity, and 98.23% F-score. The findings suggest that the proposed hybrid model could be invaluable for diagnosing breast cancer and assisting pathologists.
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