Enhancing Lung Cancer Detection: A Hybrid Approach with FbAC-NET and Bi-LRCN - K, Geetha; Elangovan, Karthikeyan (Journal of Neonatal Surgery, 2025)
Lung cancer remains a significant public health concern, necessitating accurate and efficient methods for early diagnosis. Traditional diagnostic techniques often rely on manual interpretation of medical images, leading to subjective results and increased dependency on the expertise of radiologists. In clinical practice, lung X-ray pictures are also periodically reviewed by field professionals. Here we propose a hybrid model that combines two powerful deep learning architectures. The first component of our model, FbAC-NET, is an advanced neural network based on the Feature-based Adaptive Convolutional Network (FbACN) architecture. FbAC-NET is designed to extract highly discriminative features from lung cancer images, enabling effective classification. Leveraging the advantages of FbAC-NET, we enhance the accuracy of lung cancer classification by incorporating the second component, Bi-LRCN (Bidirectional Long Short-Term Memory Recurrent Convolutional Network). Bi-LRCN is specifically tailored for sequential data analysis, making it suitable for capturing temporal dependencies and patterns in lung cancer progression. By combining the strengths of FbAC-NET and Bi-LRCN, our hybrid model achieves superior performance in multi-classification and detection of lung cancer. We conduct extensive experiments on a sizable dataset of lung cancer images. According to the results, our hybrid model performs superior to other methods. By automating the diagnosis process, our approach has the potential to enhance early detection rates, enabling timely interventions and improving patient outcomes. Utilising a Python experimental design, the system's effectiveness is then evaluated. In the LIDC/IDRI dataset, the suggested classifier performed with performance metrics of 99.89% sensitivity, 99.84% specificity, 99.25% precision, and 99.7% accuracy. For the other dataset the performance metrics were 99.63% sensitivity, 97.9% specificity, 97.6% precision, and 99.8% accuracy.