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Attention-Enhanced Hybrid Bidirectional LSTM and Temporal Convolutional Network for Early Detection of Lung Cancer in
Benjamin Appiah Yeboah1, Michael Asiedu Asare1,2, Isaac Acquah1,2
1Biomedical Technologies Lab, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
Objectives:
We developed and evaluated a lightweight, interpretable, and computationally efficient hybrid deep learning model for multiclass classification of early lung cancer in low-dose CT scans, potentially deployable in resource-limited healthcare environments.
Methods:
A novel hybrid architecture was developed that integrates Bidirectional Long Short-Term Memory (Bi-LSTM) networks, Temporal Convolutional Networks (TCNs), Efficient Channel Attention (ECA) blocks, and Local Interpretable Model-Agnostic Explanations (LIME). The model employed depthwise separable convolutions to reduce computational complexity. A multi-stream feature extraction framework was implemented to enhance interpretability and capture spatial-temporal patterns. The model was trained and validated on the IQ-OTH/NCCD dataset (version 2), containing 3,609 CT scan slices from 110 patients (1,097 original, 2,512 augmented), across three classes: normal, benign, and malignant. The dataset was split into training (60%), validation (30%), and testing (10%) subsets. Training was conducted using the AdamW optimizer for 16 epochs.
Results:
The model achieved 98.06% accuracy, 98.15% precision, 98.06% recall, and 98.04% F1-score, with a 99.88% AUC, a model size of 3.33 MB, and 279,561 parameters.
Conclusion:
The lightweight model achieves high diagnostic accuracy with computational efficiency, SHAP-based and LIME-based interpretability methods, enabling potential suitability for deployment in resource-constrained clinical settings.