Exploring Diagnostic Innovations: Survey of Deep Learning Models for Lung Disease Classification

Abstract

The diagnostic efficacy of infection categorization is meticulously assessed in this study using a variety of advanced computational models, with a particular emphasis on the improvement of initial diagnostic precision. Diagnostic medicine, particularly in the context of infectious diseases, has been significantly transformed by deep learning, a paradigm that is widely recognized as innovative in automated detection. A precisely selected array of studies examining the use of medical image classification techniques, primarily chest X-rays, in the detection of diseases such as COVID-19 and pneumonia is compiled in this in-depth study. It examines the methodological frameworks used in various studies, including sophisticated detection systems, complex classification algorithms, datasets of varying complexity, and the performance measures that accompany them. The contributions of these findings to the evolving field of medical diagnostics are meticulously assessed, attentively compared, and analytically articulated. The research also encompasses a critical comparison of previous survey works, with an emphasis on their methodological rigor, inherent limitations, and susceptibility to misuse. By outlining the strengths and limitations of a variety of methodologies, this research offers critical insights. It delineates critical measures that must be implemented to enhance clinical decision-making frameworks and intelligent diagnostic systems.