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

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.