  Detecting and classifying diseases using X-ray images is one of the more
challenging core tasks in the medical and research world. Due to the recent
high interest in radiological images and AI, early detection of diseases in
X-ray images has become notably more essential to prevent further spreading and
flatten the curve. Innovations and revolutions of Computer Vision with Deep
learning methods offer great promise for fast and accurate diagnosis of
screening and detection from chest X-ray images (CXR). This work presents rapid
detection of diseases in the lung using the efficient Deep learning pre-trained
RepVGG algorithm for deep feature extraction and classification. We used X-ray
images as an example to show the model's efficiency. To perform this task, we
classify X-Ray images into Covid-19, Pneumonia, and Normal X-Ray images. Employ
ROI object to improve the detection accuracy for lung extraction, followed by
data pre-processing and augmentation. We are applying Artificial Intelligence
technology for automatic highlighted detection of affected areas of people's
lungs. Based on the X-Ray images, an algorithm was developed that classifies
X-Ray images with height accuracy and power faster thanks to the architecture
transformation of the model. We compared deep learning frameworks' accuracy and
detection of disease. The study shows the high power of deep learning methods
for X-ray images based on COVID-19 detection utilizing chest X-rays. The
proposed framework offers better diagnostic accuracy by comparing popular deep
learning models, i.e., VGG, ResNet50, inceptionV3, DenseNet, and
InceptionResnetV2.
