Objective: The problem of automatic Myocardial Perfusion Imaging characterization for the diagnosis of Coronary Artery Disease utilising extracted Polar Maps, is considered. The recent advances in Deep Learning, and more specifically, Convolutional Neural Networks, are employed for this purpose. With Deep Learning, automatic feature extraction from images is achieved, often giving remarkable results in automatic medical image classification. For this study, Polar Maps from the database of the Laboratory of Nuclear Medicine of the University of Patras are processed. Subjects and Methods: As the initial dataset is small to train a Deep Learning model from scratch, we make use of two common strategies. The first strategy involves utilising state-of-the-art Convolutional Neural Networks, trained to extract both arbitrary and local features from images. Instead of retraining those networks from scratch, we proposed to retain the learned weights from the convolution process, and add a simple Neural Network after the convolutions, to distinguish significant and insignificant features. This procedure is called Transfer Learning. For transfer learning, we employ the CNN called VGG16, which use has been broad in medical image classification tasks. The second strategy involves data augmentation, which is achieved by rotation of the polar maps, to expand the training set. Results: We evaluate VGG16 with 3-fold cross-validation on the original set of images. VGG16 achieves an accuracy of 74%, sensitivity of 87.5%, and specificity of 51.25%. Moreover, we compare the predictions of the CNN and the doctors' predictions on the same images, with the actual labels. Our results demonstrate that the proposed model is exceeding the doctor's expertise on this particular set of images. Precisely, the doctor's characterization of the MPI images corresponds to a maximum of 70.38% accuracy, 86.02% sensitivity, and 43.75% specificity. Conclusions: Due to the small dataset at our disposal, the results do not constitute a reliable conclusion, however they are promising for future research, and can be proven valuable to assist doctors.

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