Diagnosis of hypercritical chronic pulmonary disorders using dense convolutional network through chest radiography.
Rajat MehrotraRajeev AgrawalM A AnsariPublished in: Multimedia tools and applications (2022)
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.
Keyphrases
- deep learning
- coronavirus disease
- convolutional neural network
- sars cov
- chronic obstructive pulmonary disease
- pulmonary hypertension
- artificial intelligence
- machine learning
- respiratory failure
- community acquired pneumonia
- respiratory syndrome coronavirus
- lung function
- healthcare
- drug induced
- risk assessment
- squamous cell carcinoma
- type diabetes
- intensive care unit
- public health
- computed tomography
- magnetic resonance imaging
- cystic fibrosis
- mycobacterium tuberculosis
- optical coherence tomography
- mass spectrometry
- spinal cord injury
- childhood cancer
- hepatitis c virus
- air pollution
- pulmonary tuberculosis