HCL Net: Deep Learning for Accurate Classification of Honeycombing Lung and Ground Glass Opacity in CT Images

Honeycombing Lung (HCL) is a chronic lung condition marked by advanced fibrosis, resulting in enlarged air spaces with thick fibrotic walls, which are visible on Computed Tomography (CT) scans. Differentiating between normal lung tissue, honeycombing lungs, and Ground Glass Opacity (GGO) in CT images is often challenging for radiologists and may lead to misinterpretations. Although earlier studies have proposed models to detect and classify HCL, many faced limitations such as high computational demands, lower accuracy, and difficulty distinguishing between HCL and GGO. CT images are highly effective for lung classification due to their high resolution, 3D visualization, and sensitivity to tissue density variations. This study introduces Honeycombing Lungs Network (HCL Net), a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches. HCL Net incorporates additional residual blocks, refined preprocessing techniques, and selective parameter tuning to improve classification performance. The dataset, sourced from the University Malaya Medical Centre (UMMC) and verified by expert radiologists, consists of CT images of normal, honeycombing, and GGO lungs. Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%. It also recorded strong performance in other metrics, achieving 93% precision, 100% sensitivity, 89% specificity, and an AUC-ROC score of 97%. Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net. The model significantly reduces misclassification, particularly between honeycombing and GGO lungs, enhancing diagnostic precision and reliability in lung image analysis.