{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T23:29:14Z","timestamp":1786577354817,"version":"build-2736575974"},"reference-count":66,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Biomedical Technologies Lab, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Biomed Eng Comput Biol"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  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