Enhanced BiLSTM-CNN with Adaptive Regularization for Sequential Lung Cancer CT Scan Segmentation
DOI:
https://doi.org/10.52436/1.jutif.2026.7.4.5999Keywords:
Lung Cancer, Medical Image Segmentation, Sequential CT Scan, BiLSTM, Deep LearningAbstract
Lung cancer remains one of the leading causes of cancer-related mortality worldwide, making accurate and consistent CT scan segmentation essential for early diagnosis and treatment planning. This study proposes an enhanced Bidirectional Long Short-Term Memory-Convolutional Neural Network (BiLSTM-CNN) framework for sequential lung cancer CT scan segmentation. The proposed model integrates stacked BiLSTM layers, dropout regularization, adaptive learning-rate scheduling, and an Enhanced ResNet-18 backbone to improve temporal consistency and spatial feature representation across sequential CT slices. Unlike conventional CNN-based segmentation approaches that process slices independently, the proposed framework captures inter-slice contextual dependencies to produce more stable and anatomically consistent segmentation results. The model was evaluated using a publicly available lung cancer CT scan dataset with an 80:20 training–testing split. Experimental results demonstrate that the proposed method outperformed standard LSTM and conventional BiLSTM models, achieving superior segmentation performance with a Dice Similarity Coefficient (DSC) of 0.6960 and an Intersection over Union (IoU) of 0.5337. The findings indicate that the integration of bidirectional temporal modeling and enhanced feature extraction significantly improves segmentation accuracy and generalization capability. This study contributes to the development of lightweight and efficient AI-based medical imaging systems that support more reliable lung cancer diagnosis and clinical decision-making.
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Copyright (c) 2026 Juanda Hakim Lubis, Agus Perdana Windarto, Sundari Retno Andani
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