  The ability to predict lung and heart based diseases using deep learning
techniques is central to many researchers, particularly in the medical field
around the world. In this paper, we present a unique outlook of a very familiar
problem of disease classification using X-rays. We present a hypothesis that
X-rays of patients included with the follow up history of their most recent
three chest X-ray images would perform better in disease classification in
comparison to one chest X-ray image input using an internal CNN to perform
feature extraction. We have discovered that our generic deep learning
architecture which we propose for solving this problem performs well with 3
input X ray images provided per sample for each patient. In this paper, we have
also established that without additional layers before the output
classification, the CNN models will improve the performance of predicting the
disease labels for each patient. We have provided our results in ROC curves and
AUROC scores. We define a fresh approach of collecting three X-ray images for
training deep learning models, which we have concluded has clearly improved the
performance of the models. We have shown that ResNet, in general, has a better
result than any other CNN model used in the feature extraction phase. With our
original approach to data pre-processing, image training, and pre-trained
models, we believe that the current research will assist many medical
institutions around the world, and this will improve the prediction of
patients' symptoms and diagnose them with more accurate cure.

Computer Vision and Pattern Recognition;Machine Learning