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 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.