  Lung disease is a common health problem in many parts of the world. It is a
significant risk to people health and quality of life all across the globe
since it is responsible for five of the top thirty leading causes of death.
Among them are COVID 19, pneumonia, and tuberculosis, to name just a few. It is
critical to diagnose lung diseases in their early stages. Several different
models including machine learning and image processing have been developed for
this purpose. The earlier a condition is diagnosed, the better the patient
chances of making a full recovery and surviving into the long term. Thanks to
deep learning algorithms, there is significant promise for the autonomous,
rapid, and accurate identification of lung diseases based on medical imaging.
Several different deep learning strategies, including convolutional neural
networks (CNN), vanilla neural networks, visual geometry group based networks
(VGG), and capsule networks , are used for the goal of making lung disease
forecasts. The standard CNN has a poor performance when dealing with rotated,
tilted, or other aberrant picture orientations. As a result of this, within the
scope of this study, we have suggested a vision transformer based approach end
to end framework for the diagnosis of lung disorders. In the architecture, data
augmentation, training of the suggested models, and evaluation of the models
are all included. For the purpose of detecting lung diseases such as pneumonia,
Covid 19, lung opacity, and others, a specialised Compact Convolution
Transformers (CCT) model have been tested and evaluated on datasets such as the
Covid 19 Radiography Database. The model has achieved a better accuracy for
both its training and validation purposes on the Covid 19 Radiography Database.
