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

A Deep Learning Technique using a Sequence of Follow Up X-Rays for Disease classification