Journal Article

Breast cancer classification based on hybrid CNN with LSTM model

Elshewey, Ahmed M., Ayid, Yasser M., Kaddes, Mourad, Fouad, Yasser

2025

Overview

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.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114 / 639/705 / Accuracy / Algorithms / Breast Cancer / Breast Neoplasms - classification / Breast Neoplasms - diagnosis / Breast Neoplasms - diagnostic imaging / Cancer / Cancer therapies / Classification / CNN / CNN-LSTM / Datasets / Deep Learning / Diagnosis, Computer-Assisted - methods / Female / Healthcare / Humanities and Social Sciences / Humans / Image processing / Long short-term memory / LSTM / Malignancy / Mammography / Mammography - methods / multidisciplinary / Neural networks / Neural Networks, Computer / Science / Science (multidisciplinary) / Sensitivity analysis