Diagnosis of hypercritical chronic pulmonary disorders using dense convolutional network through chest radiography

• Published in: Multimedia tools and applications Vol. 81; no. 6; pp. 7625 - 7649
• Main Authors: Mehrotra, Rajat, Agrawal, Rajeev, Ansari, M. A.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2022; Springer Nature B.V
• Subjects: Artificial neural networks; Asthma; Automation; Bacterial diseases; Cancer; Chest; Chronic obstructive pulmonary disease; Computer Communication Networks; Computer Science; Coronaviruses; COVID-19; Data Structures and Information Theory; Diagnosis; Disorders; Fibrosis; Health services; Image classification; Lung diseases; Lungs; Machine learning; Medical imaging; Multimedia Information Systems; Pneumonia; Special Purpose and Application-Based Systems; Tuberculosis; Viral diseases; X-rays; COVID-19; Deep learning; Pneumonia; CNN; Chest x-ray (CXR); Chronic pulmonary disorders
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-021-11748-5

Lung-related ailments are prevalent all over the world which majorly includes asthma, chronic obstructive pulmonary disease (COPD), tuberculosis, pneumonia, fibrosis, etc. and now COVID-19 is added to this list. Infection of COVID-19 poses respirational complications with other indications like cough, high fever, and pneumonia. WHO had identified cancer in the lungs as a fatal cancer type amongst others and thus, the timely detection of such cancer is pivotal for an individual’s health. Since the elementary convolutional neural networks have not performed fairly well in identifying atypical image types hence, we recommend a novel and completely automated framework with a deep learning approach for the recognition and classification of chronic pulmonary disorders (CPD) and COVID-pneumonia using Thoracic or Chest X-Ray (CXR) images. A novel three-step, completely automated, approach is presented that first extracts the region of interest from CXR images for preprocessing, and they are then used to detects infected lungs X-rays from the Normal ones. Thereafter, the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD), which might be utilized in the current scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases. And finally, highlight the regions in the CXR which are indicative of severe chronic pulmonary disorders like COVID-19 and pneumonia. A detailed investigation of various pivotal parameters based on several experimental outcomes are made here. This paper presents an approach that detects the Normal lung X-rays from infected ones and the infected lung images are further classified into COVID-pneumonia, pneumonia, and other chronic pulmonary disorders with an utmost accuracy of 96.8%. Several other collective performance measurements validate the superiority of the presented model. The proposed framework shows effective results in classifying lung images into Normal, COVID-pneumonia, pneumonia, and other chronic pulmonary disorders (OCPD). This framework can be effectively utilized in this current pandemic scenario to help the radiologist in substantiating their diagnosis and in starting well in time treatment of these deadly lung diseases.