A Comparative Study of Deep Learning Models in Medical Image Analysis for Pulmonary Disease Detection: A Review (Don Bosco Institute of Technology Delhi Journal of Research, 2025)
This survey provides the view of current scenario of Deep Learning models used  to detect pulmonary diseases, Stroke Disease and some other diseases related to  blood clots. The diagnosis and categorization of lung disorders using medical  images, especially chest X-rays and CT scans, has been greatly improved by recent  developments in deep learning. This survey highlights the use of deep learning  techniques in the detection of lung diseases by compiling and analyzing 20  research publications published between 2016 and 2020. By offering a thorough  taxonomy based on seven essential characteristics—image kinds, features, data  augmentation, deep learning models, transfer learning, ensemble methods, and  lung disease types—this study fills that gap. Convolutional neural networks  (CNNs) dominate, transfer learning models like VGG, ResNet, and Inception-V3  are widely used, and diseases including COVID-19, TB, and pneumonia are the  main emphasis, according to key findings. The paper also indicates promising  avenues for future research, such as combining clinical and demographic data,  creating lightweight models for contexts with limited resources, and implementing  ensemble learning techniques.
Keywords: scenario; Pneumonia; Pulmonary; COVID-19