article · Scientific Reports
Early and rapid detection of breast cancer remains essential for effective clinical treatment. To support computer-aided diagnosis and assist less experienced clinicians, an artificial intelligence architecture has been developed that integrates a Convolutional Neural Network with a Long Short-Term Memory network. The Convolutional Neural Network extracts spatial hierarchies and malignancy patterns from mammographic images, while the recurrent network captures sequential dependencies and temporal interactions. Tested on two public datasets, the hybrid model was evaluated alongside standalone architectures including VGG-16, RESNET-50, and Gated Recurrent Units. Using evaluation metrics such as sensitivity, specificity, F-score, and area under the curve, the combined model outperformed alternative configurations, securing classification accuracies of 99.17 percent and 99.90 percent across the two image collections.
Breast cancer is a major global health challenge where rapid, reliable identification can determine treatment success. Automated diagnostic tools can help bridge gaps in clinical expertise by assisting less experienced healthcare professionals in interpreting complex mammograms. Demonstrating that combined deep learning architectures can achieve near-perfect classification on test datasets provides a strong technical basis for building more dependable diagnostic support software.
The technology offers potential integration into computer-aided diagnosis software designed to assist radiologists and less experienced clinicians with mammographic image analysis. As the results are based on retrospective tests using two public repositories, the research represents an early to applied development stage. Real-world commercial deployment would require prospective clinical validation across diverse hospital imaging systems to verify the model outside open-access dataset environments.
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Breast cancer (BC) is a global problem, largely due to a shortage of knowledge and early detection. The speed-up process of detection and classification is crucial for effective cancer treatment. Medical image analysis methods and computer-aided diagnosis can enhance this process, providing training and assistance to less experienced clinicians. Deep Learning (DL) models play a great role in accurately detecting and classifying cancer in the huge dataset, especially when dealing with large medical images. This paper presents a novel hybrid model of DL models combined a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for binary breast cancer classification on two datasets available at the Kaggle repository. CNNs extract mammographic features, including spatial hierarchies and malignancy patterns, whereas LSTM networks characterize sequential dependencies and temporal interactions. Our method combines these structures to improve classification accuracy and resilience. We compared the proposed model with other DL models, such as CNN, LSTM, Gated Recurrent Units (GRUs), VGG-16, and RESNET-50. The CNN-LSTM model achieved superior performance with accuracies of 99.17% and 99.90% on the respective datasets. This paper uses prediction evaluation metrics such as accuracy, sensitivity, specificity, F-score, and the AUC curve. The results showed that our model CNN-LSTM can enhance the performance of breast cancer classifiers compared with others with 99.90% accuracy on the second dataset.
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DOI: 10.1038/s41598-025-88459-6
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