Post-COVID Chest Disease Monitoring Using Self Adaptive Convolutional Neural Network

Review Article

Austin J Pulm Respir Med. 2023; 10(1): 1096.

Post-COVID Chest Disease Monitoring Using Self Adaptive Convolutional Neural Network

Vamsi Kakani¹; B Varun¹; Jyostna Devi Bodapati¹*; Konda Raja Sekhar²

¹Dept of ACSE, VFSTR Deemed to be University, Vadlamudi, India

²IEEE Senior Member, India

*Corresponding author: Jyostna Devi Bodapati Department of ACSE, VFSTR Deemed to be University, Vadlamudi, India. Email: jyostna.bodapati82@gmail.com

Received: March 28, 2023 Accepted: May 05, 2023 Published: May 12, 2023

Abstract

Due to the post-Covid effect, several people are experiencing respiratory problems, with Pneumonia being a common and frequent issue. Chest X-rays can easily identify this, but regular monitoring is necessary for older people with respiratory issues. To address this issue, we have developed a model that predicts four different class of chest diseases: Covid, Pneumonia, Tuberculosis, and Normal. The proposed transfer learning based convolutional neural network model utilizes a collaborative dataset consisting of all these four disease X-ray images and is trained using the early stopping approach to ensure optimal performance. The self-adaptive CNN achieves an accuracy of 95-97% on the train split and 90-95% on the test split. Our model performs well on the collaborative dataset of chest X-ray images, making it an effective tool for identifying respiratory problems in post-Covid patients and elder oldage people.

Keywords: Transfer learning; Lung Infection; Covid detection

Introduction

The COVID-19 pandemic is ongoing, and the long-term effects of the disease can weaken the respiratory system, leading to pneumonia, asthma, and chest pain, especially during cold weather. Regular check-ups are necessary to monitor these conditions and manually conducting these checkups may not be practical. In order to simplify the diagnostic process, automated tools can be used to support the physicians. It is true that medical imaging techniques can present several challenges for accurate diagnosis and treatment, and there has been a growing interest in applying deep learning methods to overcome these difficulties.

Since 2020, several inquiries about establishing approaches and models to separate those who are at risk for these diseases from those who are not. Most of these studies use Convolutional Neural Networks (CNNs) to classify chest X-rays as normal or abnormal, enabling the identification of possible chest disease cases. Not only for the chest diseases, CNNs often yield high accuracy for several disease recognition tasks such as diabetic retinopathy recognition, pediatric disease identification, tumor prediction and many other real-time problems. CNNs are good at learning hidden features from the images through multiple hidden layers and offers better image representations compared to the conventional image representations.

CNNs have shown superior performance for various real-time tasks and are far better than fully connected Deep Neural Networks (DNNs) for chest pathology identification. The use of deep CNN-based architectures such as Inception V3 has shown promising results in the categorization of skin cancer, as demonstrated in Andre's study. Similarly, Grewal's work on detecting brain haemorrhage in computed tomography images and Lakhani's approach for automated categorization of pulmonary TB using a neural network-based approach (CNN) have also shown potential for improving medical image analysis. CNNs with deep architectures need large data for training which can be considered as a weakness of CNN for their application in disease recognition. In such cases, transfer learning approaches are preferrable to training of a deep CNN model from scratch.Razaaket et al’s work emphasized on the presenting various challenges involved in medical imaging, such as low image resolution, noise, artifacts, and variability in the appearance of anatomical structures and pathological conditions. Additionally, accurate prediction of the presence or absence of the disease in the images was demonstrated as it involves a simple binary classification task. Accurately predicting different types of diseases from medical images is a complex multi-class classification task, and most of the existing works are not suitable for this purpose as it is complex compared to the earlier task.

With the requirement of the range of common diseases, we focus on developing an efficient model to process the chest X-ray images and classify them into COVID-19, pneumonia, and tuberculosis categories. The proposed model uses different types of image pre-processing techniques such as scaling, resizing and then extracts deep features from chest X-ray images. Deep pre-trained Convolutional Neural Network (CNN) models are used for feature extraction. The proposed approach shows promising in addressing the challenges of medical imaging techniques and improves the accuracy and efficiency of medical image analysis. The experimental studies carried out on benchmark dataset leads to the validation accuracy of 93.15%. The proposed model is also trained on cross data sets and merged datasets, including chest X-ray pneumonia, tuberculosis TB chest X-ray dataset, chest X-ray COVID-19 pneumonia, and COVID-19 chest X-ray dataset.

Literature Survey

The pipeline of image based respiratory disease prediction models involve image pre-processing techniques aiming for improvement of the image data that suppresses unwilling distortions and enhances image features important for further processing. This follows the deep neural network model training.

It's common for researchers to compare different deep learning models and techniques for the task of lung disease prediction using Chest X-ray datasets. Table 1 provides a summary of recent research in this area, highlighting the performance of different models and techniques. These studies typically compare different deep learning models and techniques and evaluate their performance on Chest X-ray datasets.