Abstract:
In the very recent past, Infectious disease-related sickness has long posed a concern on a global scale. Each year, COVID-19, pneumonia, and tuberculosis (TB) cause millions of deaths because they all affect the lungs. Early detection and diagnosis can help provide chances for better care in all circumstances. A low-cost, simple imaging approach called chest X-ray (CXR) imaging can be used to detect and screen lung abnormalities brought on by infectious diseases such as Covid-19, pneumonia, and tuberculosis. This paper provided a thorough analysis of current deep-learning methods for diagnosing Covid-19, pneumonia, and TB. According to the research papers reviewed, the most widely used deep learning algorithm for detecting Covid-19, pneumonia, and TB from chest X-ray (CXR) pictures is Deep Convolutional Neural Network (DCNN). We compared the proposed DNN to well-known DNNs like EfficientNetB0, DenseNet169, and DenseNet201 in order to more accurately assess how well it performed. Our findings are equivalent to the state-of-the-art, and since the proposed CNN is lightweight, it may be employed for widespread screening in areas with limited resources. From three diverse publicly accessible datasets merged into one dataset, the proposed DNN produced the following accuracies for that dataset: 99.15%, 98.89%, and 97.79% for EfficientNetB0, DenseNet169, and DenseNet201 respectively. The proposed network can help radiologists make quick and accurate diagnoses because it is effective at identifying COVID-19 and other lung infectious diseases using chest X-ray imagery.This paper also gives young scientists a good insight on how to create CNN models that are highly efficient when used with medical images to identify diseases early.
