Detection and classification of lung cancer using sequential hybridization of CNN and RNN type architectures

Early and accurate lung cancer detection from computed tomography (CT) images remains a challenging task because of the complex morphology of lung nodules, class imbalance, variation in image quality, and the risk of overfitting in deep learning models. Conventional manual interpretation is time-consuming and may be affected by inter-observer variability. Therefore, an automated and reliable CT-based classification framework is required to support early identification of benign, malignant, and normal lung conditions.

This study proposes a sequential hybrid deep learning framework that integrates convolutional and recurrent neural network components for multiclass lung cancer classification. A dataset of 1,600 CT-scan images collected from multiple hospital data repositories across Bengaluru was used and divided in an approximate 70:30 ratio for training and validation. The preprocessing pipeline includes contrast enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE), morphological operations for lung segmentation, and nodule-focused masking to isolate diagnostically relevant lung regions. Data augmentation and transfer learning were applied to improve model generalization and reduce overfitting. DenseNet201 was used for feature extraction, while a bidirectional gated recurrent unit (BiGRU) module was incorporated for sequential representation learning. Hyperparameter optimization and early stopping were used to improve training stability and classification performance.

The proposed DenseNet201-BiGRU sequential hybrid architecture achieved an overall accuracy of 95.8%. The class-wise accuracies were 97.33% for benign cases, 93.33% for malignant cases, and 96.67% for normal cases. Precision, recall, and F1-score values further demonstrated that the model maintained reliable classification performance across diverse and imbalanced CT image classes.

The results indicate that sequential hybridization of DenseNet201-based feature extraction with BiGRU-based representation learning provides an efficient, precise, and robust framework for CT-based lung cancer classification. The proposed method improves classification reliability by combining enhanced preprocessing, focused lung-region extraction, transfer learning, and recurrent modeling. However, further validation using larger, multi-center datasets and additional clinical testing is required before real-world deployment and broader diagnostic application.