Segmenting and classifying lung diseases with M-Segnet and Hybrid Squeezenet-CNN architecture on CT images

• Published in: PloS one Vol. 19; no. 5; p. e0302507
• Main Authors: Shafi, Syed Mohammed, Chinnappan, Sathiya Kumar
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.05.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Biology and Life Sciences; Classification; Classifiers; Computed tomography; Computer and Information Sciences; COVID-19; CT imaging; Datasets; Deep learning; Disease transmission; Efficiency; Engineering and Technology; Feature extraction; Humans; Image processing; Image Processing, Computer-Assisted - methods; Image quality; Image segmentation; Infections; Lung - diagnostic imaging; Lung - pathology; Lung diseases; Lung Diseases - classification; Lung Diseases - diagnostic imaging; Medical imaging; Medical imaging equipment; Medicine and Health Sciences; Neural Networks, Computer; Physical Sciences; Power spectral density; Preprocessing; Public health; Research and Analysis Methods; Segmentation; Tomography, X-Ray Computed - methods; Tumors; Wiener filtering

Diagnosing lung diseases accurately and promptly is essential for effectively managing this significant public health challenge on a global scale. This paper introduces a new framework called Modified Segnet-based Lung Disease Segmentation and Severity Classification (MSLDSSC). The MSLDSSC model com...