Advancing Pulmonary Infection Diagnosis: A Comprehensive Review of Deep Learning Approaches in Radiological Data Analysis

Abstract

Early detection of infectious lung diseases is vital, and various researchers have created models to help with this. Different experts may have different opinions about how to classify a particular image in the dataset. The expertise, level of experience, or personal preferences of the experts might be the source of these differences. Automatic disease classification can help radiologists by reducing workload and improving patient care. Recent advancements in deep learning have helped the diagnosis and classification of lung diseases in medical imaging. As a result, there are several research in the literature utilising deep learning to identify lung diseases. A comprehensive review of the most recent DL and ML methods for lung disease diagnosis is given in this work. The selected studies are published from 2019 until 2024. Overall, total seventy-seven carefully chosen papers from various publications, including Nature, IEEE, Springer, Elsevier, and Wiley, are included in this study. Deep learning techniques for the detection of infectious lung diseases from medical images are presented in this paper. In addition to providing a taxonomy of the most advanced deep learning and machine learning-based lung disease detection systems, this comprehensive review also seeks to identify existing challenges, present the trends in the field’s current research, and provide projections about potential future directions.