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
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0302507

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 comprises four phases: "preprocessing, segmentation, feature extraction, and classification." Initially, the input image undergoes preprocessing using an improved Wiener filter technique. This technique estimates the power spectral density of the noisy and original images and computes the SNR assisted by PSNR to evaluate image quality. Next, the preprocessed image undergoes Segmentation to identify and separate the RoI from the background objects in the lung image. We employ a Modified Segnet mechanism that utilizes a proposed hard tanh-Softplus activation function for effective Segmentation. Following Segmentation, features such as MLDN, entropy with MRELBP, shape features, and deep features are extracted. Following the feature extraction phase, the retrieved feature set is input into a hybrid severity classification model. This hybrid model comprises two classifiers: SDPA-Squeezenet and DCNN. These classifiers train on the retrieved feature set and effectively classify the severity level of lung diseases.