Abstract:
The chest X-ray abnormal and normal classification model will classify X-ray images of
patients. The dataset is collected from hospitals for training. The state of art image classification
algorithm (YOLOv5x-cls) was used to train the model. The model will classify the scanned Xray
into normal and abnormal Chest X-rays. As there is a huge burden on a radiologist of 3Million
X-rays per annum, our project will help them to get the report in a single click. The project is a
breakthrough in radiology, due to the instant rise in diseases, doctors found it difficult to tackle
them. So, our project will bring more ease to doctors, which will save time and help in
accuracy doctors need to treat more patients in less time. The model is trained to achieve maximum
Accuracy (83%) on the dataset. The trained model is used in a web app for online inference and
readily results can be served to doctors in it.
