Deep Learning Approaches for Classification of Honeycombing and Ground Glass Opacity in Lung CT Imaging: A Systematic Review.
Authors
Affiliations (4)
Affiliations (4)
- Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia.
- Department of Computing, University of Roehampton, Roehampton Lane, London SW15 5PH, UK.
- Department of Biomedical Imaging, University Malaya Medical Centre, Kuala Lumpur 50603, Malaysia.
- Faculty of Computer Information Science, Higher Colleges of Technology, Al-Faseel Campus, Fujairah P.O. Box 1626, United Arab Emirates.
Abstract
Lung diseases such as Honeycombing (HC) and Ground Glass Opacity (GGO) present significant challenges in medical image analysis due to their overlapping visual characteristics and complex structural patterns in Computed Tomography (CT) images. These conditions often exhibit similar appearances to normal lung tissue, making accurate differentiation difficult and highly dependent on imaging quality and expert interpretation. This paper presents a systematic literature review of studies published between 2011 and 2026 on lung disease detection and classification using medical imaging. A total of 98 selected studies is analyzed to examine recent developments in Machine Learning (ML) and Deep Learning (DL) techniques for automated lung disease analysis. The review covers key aspects including imaging modalities such as CT and chest X-ray, publicly available and private datasets, preprocessing techniques, feature extraction methods, classification models, and evaluation metrics. The findings indicate that traditional machine learning approaches were widely used in earlier studies, while recent research has increasingly shifted towards deep learning methods due to their ability to automatically learn discriminative features from medical images. Among these approaches, Convolutional Neural Networks (CNNs), ResNet-based architectures, DenseNet, and EfficientNet are the most commonly adopted models for lung disease classification. Despite significant advancements, several challenges remain, including limited availability of publicly accessible datasets, class imbalance, variability in imaging protocols, and lack of standardized evaluation frameworks. In addition, misclassification between visually similar conditions such as Honeycombing and GGO remains a persistent issue that affects diagnostic reliability. The review identifies key research gaps and highlights the need for standardized datasets, improved preprocessing techniques, and consistent evaluation protocols. Future research should focus on enhancing model generalisation and robustness across diverse datasets to support reliable automated lung disease diagnosis in clinical practice.