A Comparative Study of Deep Learning Models in Medical Image Analysis for Pulmonary Disease Detection: A Review
DOI:
https://doi.org/10.48165/dbitdjr.2025.2.01.01Keywords:
scenario, Pneumonia, Pulmonary, COVID-19Abstract
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.
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