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Today’s world Coronary artery disease is the most common cause of death worldwide and thus early diagnosis. Well-timed opportune of this disease can lead to significant reduction in its morbidityand mortality in both younger and older for angiogram test. In this research multi slice CT scanner is used for heart angiogram test. With the help of this multi slice CT angiogram image we detect the hart diseased or not. For this disease identification and classification of angiogram images many machine learning algorithms are previously proposed those are SVM RBF and RBF neural network. Problem with SVM isnon-liner method when use any type of application will miss most liner ways of blood vessels and lack of speed in process. For non linear classification we are using RBF SVM. Problem with RBF neural network is not solve the hierarchal and component based problems, so resolve the problem using deep learning. This issue drastically improves the estimation efficiency for real time application. This methodology consumes less time for both learning as well as testing comparatively than any other methods. This issue highly improves the estimation efficiency and accuracy for real time 256, 512 slices CT scan angiogram image.

Determination with Deep Learning and One Layer Neural Network for Image Processing in MultiSlice CT Angiogram