Breast cancer classification based on hybrid CNN with LSTM model
Abstract 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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-88459-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823862337383170048 |
|---|---|
| author | Mourad Kaddes Yasser M. Ayid Ahmed M. Elshewey Yasser Fouad |
| author_facet | Mourad Kaddes Yasser M. Ayid Ahmed M. Elshewey Yasser Fouad |
| author_sort | Mourad Kaddes |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-bed3a7e0e6a8460c85a38b4f71c2b18f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-bed3a7e0e6a8460c85a38b4f71c2b18f2025-02-09T12:34:57ZengNature PortfolioScientific Reports2045-23222025-02-0115111410.1038/s41598-025-88459-6Breast cancer classification based on hybrid CNN with LSTM modelMourad Kaddes0Yasser M. Ayid1Ahmed M. Elshewey2Yasser Fouad3Department of Information Systems, College of Computing & Information Technology at Khulais, University of JeddahMathematics Department, Applied Collage Al-Kamil Branch, University of JeddahDepartment of Computer Science, Faculty of Computers and Information, Suez UniversityDepartment of Computer Science, Faculty of Computers and Information, Suez UniversityAbstract 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.https://doi.org/10.1038/s41598-025-88459-6Breast CancerCNNLSTMCNN-LSTMDLHealthcare |
| spellingShingle | Mourad Kaddes Yasser M. Ayid Ahmed M. Elshewey Yasser Fouad Breast cancer classification based on hybrid CNN with LSTM model Scientific Reports Breast Cancer CNN LSTM CNN-LSTM DL Healthcare |
| title | Breast cancer classification based on hybrid CNN with LSTM model |
| title_full | Breast cancer classification based on hybrid CNN with LSTM model |
| title_fullStr | Breast cancer classification based on hybrid CNN with LSTM model |
| title_full_unstemmed | Breast cancer classification based on hybrid CNN with LSTM model |
| title_short | Breast cancer classification based on hybrid CNN with LSTM model |
| title_sort | breast cancer classification based on hybrid cnn with lstm model |
| topic | Breast Cancer CNN LSTM CNN-LSTM DL Healthcare |
| url | https://doi.org/10.1038/s41598-025-88459-6 |
| work_keys_str_mv | AT mouradkaddes breastcancerclassificationbasedonhybridcnnwithlstmmodel AT yassermayid breastcancerclassificationbasedonhybridcnnwithlstmmodel AT ahmedmelshewey breastcancerclassificationbasedonhybridcnnwithlstmmodel AT yasserfouad breastcancerclassificationbasedonhybridcnnwithlstmmodel |