Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification
Abstract Breast cancer detection remains one of the most challenging problems in medical imaging. We propose a novel hybrid model that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and EfficientNet-B0, a pre-trained model. By leveraging Eff...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-95311-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850184099664232448 |
|---|---|
| author | Umesh Kumar Lilhore Yogesh Kumar Sharma Brajesh Kumar Shukla Muniraju Naidu Vadlamudi Sarita Simaiya Roobaea Alroobaea Majed Alsafyani Abdullah M. Baqasah |
| author_facet | Umesh Kumar Lilhore Yogesh Kumar Sharma Brajesh Kumar Shukla Muniraju Naidu Vadlamudi Sarita Simaiya Roobaea Alroobaea Majed Alsafyani Abdullah M. Baqasah |
| author_sort | Umesh Kumar Lilhore |
| collection | DOAJ |
| description | Abstract Breast cancer detection remains one of the most challenging problems in medical imaging. We propose a novel hybrid model that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and EfficientNet-B0, a pre-trained model. By leveraging EfficientNet-B0, which has been trained on the large and diverse ImageNet dataset, our approach benefits from transfer learning, enabling more efficient feature extraction from mammographic images compared to traditional methods that require CNNs to be trained from scratch. The model further enhances performance by incorporating Bi-LSTM, which allows for processing temporal dependencies in the data, which is crucial for accurately detecting complex patterns in breast cancer images. We fine-tuned the model using the Adam optimizer to optimize performance, significantly improving accuracy and processing speed. Extensive evaluation of well-established datasets such as CBIS-DDSM and MIAS resulted in an outstanding 99.2% accuracy in distinguishing between benign and malignant tumors. We also compared our hybrid model to other well-known architectures, including VGG-16, ResNet-50, and DenseNet169, using three optimizers: Adam, RMSProp, and SGD. The Adam optimizer consistently achieved the highest accuracy and lowest loss across the training and validation phases. Additionally, feature visualization techniques were applied to enhance the model’s interpretability, providing deeper insight into the decision-making process. The Proposed hybrid model sets a new standard in breast cancer detection, offering exceptional accuracy and improved transparency, making it a valuable tool for clinicians in the fight against breast cancer. |
| format | Article |
| id | doaj-art-749324d782e6446fb1cbda23ef137332 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-749324d782e6446fb1cbda23ef1373322025-08-20T02:17:09ZengNature PortfolioScientific Reports2045-23222025-04-0115112710.1038/s41598-025-95311-4Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classificationUmesh Kumar Lilhore0Yogesh Kumar Sharma1Brajesh Kumar Shukla2Muniraju Naidu Vadlamudi3Sarita Simaiya4Roobaea Alroobaea5Majed Alsafyani6Abdullah M. Baqasah7Department of CSE, School of Computing Science and Engineering, Galgotias UniversityDepartment of Computer Science and Engineering, Koneru Lakshmaiah Education FoundationDepartment: Computer Engineering and Application, GLA UniversityComputer Science and Engineering, Koneru Lakshmaiah Education FoundationDepartment of CSE, School of Computing Science and Engineering, Galgotias UniversityDepartment of Computer Science, College of Computers and Information Technology, Taif UniversityDepartment of Computer Science, College of Computers and Information Technology, Taif UniversityDepartment of Information Technology, College of Computers and Information Technology, Taif University, P. O. Box 11099Abstract Breast cancer detection remains one of the most challenging problems in medical imaging. We propose a novel hybrid model that integrates Convolutional Neural Networks (CNNs), Bidirectional Long Short-Term Memory (Bi-LSTM) networks, and EfficientNet-B0, a pre-trained model. By leveraging EfficientNet-B0, which has been trained on the large and diverse ImageNet dataset, our approach benefits from transfer learning, enabling more efficient feature extraction from mammographic images compared to traditional methods that require CNNs to be trained from scratch. The model further enhances performance by incorporating Bi-LSTM, which allows for processing temporal dependencies in the data, which is crucial for accurately detecting complex patterns in breast cancer images. We fine-tuned the model using the Adam optimizer to optimize performance, significantly improving accuracy and processing speed. Extensive evaluation of well-established datasets such as CBIS-DDSM and MIAS resulted in an outstanding 99.2% accuracy in distinguishing between benign and malignant tumors. We also compared our hybrid model to other well-known architectures, including VGG-16, ResNet-50, and DenseNet169, using three optimizers: Adam, RMSProp, and SGD. The Adam optimizer consistently achieved the highest accuracy and lowest loss across the training and validation phases. Additionally, feature visualization techniques were applied to enhance the model’s interpretability, providing deeper insight into the decision-making process. The Proposed hybrid model sets a new standard in breast cancer detection, offering exceptional accuracy and improved transparency, making it a valuable tool for clinicians in the fight against breast cancer.https://doi.org/10.1038/s41598-025-95311-4Transfer learningBreast Cancer predictionCNN-BiLSTM model; Adam optimizationPrecision medicine; medical imaging |
| spellingShingle | Umesh Kumar Lilhore Yogesh Kumar Sharma Brajesh Kumar Shukla Muniraju Naidu Vadlamudi Sarita Simaiya Roobaea Alroobaea Majed Alsafyani Abdullah M. Baqasah Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification Scientific Reports Transfer learning Breast Cancer prediction CNN-BiLSTM model; Adam optimization Precision medicine; medical imaging |
| title | Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification |
| title_full | Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification |
| title_fullStr | Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification |
| title_full_unstemmed | Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification |
| title_short | Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification |
| title_sort | hybrid convolutional neural network and bi lstm model with efficientnet b0 for high accuracy breast cancer detection and classification |
| topic | Transfer learning Breast Cancer prediction CNN-BiLSTM model; Adam optimization Precision medicine; medical imaging |
| url | https://doi.org/10.1038/s41598-025-95311-4 |
| work_keys_str_mv | AT umeshkumarlilhore hybridconvolutionalneuralnetworkandbilstmmodelwithefficientnetb0forhighaccuracybreastcancerdetectionandclassification AT yogeshkumarsharma hybridconvolutionalneuralnetworkandbilstmmodelwithefficientnetb0forhighaccuracybreastcancerdetectionandclassification AT brajeshkumarshukla hybridconvolutionalneuralnetworkandbilstmmodelwithefficientnetb0forhighaccuracybreastcancerdetectionandclassification AT munirajunaiduvadlamudi hybridconvolutionalneuralnetworkandbilstmmodelwithefficientnetb0forhighaccuracybreastcancerdetectionandclassification AT saritasimaiya hybridconvolutionalneuralnetworkandbilstmmodelwithefficientnetb0forhighaccuracybreastcancerdetectionandclassification AT roobaeaalroobaea hybridconvolutionalneuralnetworkandbilstmmodelwithefficientnetb0forhighaccuracybreastcancerdetectionandclassification AT majedalsafyani hybridconvolutionalneuralnetworkandbilstmmodelwithefficientnetb0forhighaccuracybreastcancerdetectionandclassification AT abdullahmbaqasah hybridconvolutionalneuralnetworkandbilstmmodelwithefficientnetb0forhighaccuracybreastcancerdetectionandclassification |