Lung Cancer Detection: Advancing CT Image Analysis Through Hybrid Bidirectional Long Short-Term Memory and Recurrent Neural Network

Abstract

Globally, lung cancer (LC) is the leading cause of death from cancer. Medical image analysis based on deep learning (DL) has strong potential for detecting and diagnosing lung cancer by identifying early symptoms with image aid from positron emission tomography (PET) and computed tomography (CT). The majority of DL models created for LC detection are very resource-intensive, requiring a great deal of computational capacity; hence, they pose a challenge to deployment on a standard clinical system and are therefore significantly less accessible in resource-constrained settings. This additional computational load may delay diagnosis and treatment, thus affecting the outcome of the patients. Therein lies the critical need for developing more lightweight and efficient deep learning models that ensure high accuracy while reducing computational requirements. This manuscript presents a Lung Cancer Detection technique, LCD-CT-BiLSTM-RNN, based on advanced CT image analysis. First, noise reduction in the lung CT images by anisotropic guided filtering (AGF) is performed. Then, adaptive fuzzy K-means clustering (AFKMC) separates the affected areas of cancer, and Synchroextracting Transform (SET) adds the spectral features. Finally, a hybrid BiLSTM and RNN architecture runs the classification task with an improved overall accuracy. Hybrid optimization using Slime Mould Optimization (SMO) and Golden Eagle Optimization (GEO) fine-tunes the model. The performance of the methods is assessed using MATLAB's accuracy, precision, recall, F1-score, specificity, Matthews Correlation Coefficient (MCC), and ROC to compare the acquired findings with the existing approaches. The performance of the proposed method provides 2.03%, 3.45%, and 2.36% higher accuracy compared with existing techniques like Fuzzy Particle Swarm Optimization with Convolutional Neural Network for Detection of LC (FPSO-CNN), Deep learning Instantaneously Accomplished Neural Network with Improved Profuse Clustering Technique for LC detection (DITNN-IPCT), and Residual Learning Denoising Model with Convolutional Neural Network (DR-Net-CNN) for the Detection of LC.