A Hybrid Graph Convolutional Bidirectional Lstm Model Based Classifier And Segmentation Using Modified Birch Algorithm For Early Detection Of Lung Cancer - Priya, M. Vaishnava; R. Tamilselvi, R. Tamilselvi (Journal of Neonatal Surgery, 2025)
Lung cancer (LC) has an unbelievable annual incidence of over five million deaths, making it one of the leading causes of mortality worldwide for both men and women. Detecting malignant lung nodules (LN) on the provided input lung image and classifying the LC along with its severity are the primary objectives of this study. Using cutting-edge Hybrid Deep learning (HDL) techniques, this study detects the malignant LNs. This research proposes an intelligent framework for lung cancer detection in PET images, integrating advanced techniques for noise removal, segmentation, classification, and hyperparameter tuning. First, aAdaptive Weiner Filter (AWF) is applied to PET images to effectively remove noise and enhance image clarity, ensuring more accurate analysis. Subsequently, the Modified BIRCH algorithm is utilized for segmentation, enabling the delineation of regions of interest within the images. For LC classification, a Hybrid Graph Convolutional Bidirectional Long Short-Term Memory (Bi-LSTM)framework is developed. This novel architecture combines the effectiveness of Graph Convolutional Networks (GCN) and Bi-LSTM units, facilitating comprehensive analysis of spatial and temporal features in PET images. To enhance model performance, Hyperparameter (HP) tuning is performed using the Enhanced HBO (Honey bee optimization) Algorithm, optimizing model parameters for improved accuracy and robustness. The suggested framework is evaluated using the PET Dataset, demonstrating its effectiveness in accurately detecting LC in PET images