Optimized theory-guided convolutional neural network for lung cancer classification using CT images with advanced FPGA implementation

Lung carcinoma, commonly referred to as lung cancer is a severe disease with higher global mortality rate. The uncontrolled growth of cells in lung tissues is the reason for it. Detecting and treating lung cancer early is important for curing it, and diagnostic methods commonly include Computed Tomography (CT) scans and blood tests. However, accurately detecting and classifying pulmonary nodules in CT images remains a challenge due to the complexity of the data, higher computational demands,require for real-time processing. Existing systems often face limitations, such as high power consumption, prolonged processing times, and scalability issues, reducing their effectiveness in clinical environments. To overcome these challenges, this manuscript proposes an Optimized Theory-Guided Convolutional Neural Network for Lung Cancer Classification utilizing CT Images with Advanced FPGA Implementation (OTCNN-LCT-FPGA). Computed Tomography Image (CTI) from the LIDC-IDRI dataset are pre-processed using Variational Bayesian Robust Adaptive Filtering (VBRAF) technique, which removes noise and converts RGB images into binary format. The pre-processed images are classified as benign or malignant using Theory-Guided Convolutional Neural Network (TCNN). The Polar Coordinate Bald Eagle Search Algorithm (PBESA) is introduced to enhance the weight parameters of TCNN method while reducing resource utilization and increasing processing speed. The TCNN classifier is executed on Field-Programmable Gate Array (FPGA) to further decrease the computation time. The proposed OTCNN-LCT-FPGA method demonstrates significant improvements. How if, it achieves 6.26 %, 7.22 %, and 5.27 higher specificity and 2.96 %, 3.46 %, and 5.80 % higher F1-Score when compared to the existing methods, such as FCFNN-LCC, ISNeT-DLC-CT and DNNLCC-EOS respectively.