A method employing a computing device for providing a global cardiac wall motion classification or evaluation for a patient based on Cardiac Magnetic Resonance CMR image data acquired from the patient, wherein the CMR image data comprise a plurality of image frames, I(x, y, z, t) , acquired for respective two-dimensional slices in at least one longitudinal direction, z, of the patient's heart and for a plurality of times, t, wherein the computing device comprises at least one non-transitory computer readable medium configured to store instructions executable by at least one processor to carry out steps associated with the method, the method comprising: a myocardium segmentation step (S10) of inputting the plurality of image frames into two or more neural networks each trained to predict or determine in the image frames a portion corresponding to muscle tissue and a portion corresponding to surrounding tissue of the heart muscle, applying the trained neural networks in parallel, and fusing an output of each of the trained neural networks into a single output indicating a segmentation, for each of the plurality of image frames, between a first portion indicating muscle tissue of the heart and a second portion indicating surrounding tissue of the heart muscle, and determining a corresponding mask of muscle tissue for the first portion; a slice classification step (S20) of assigning each of the plurality of image frames in each slice, z, to an anatomic layer of the heart; a movement feature extraction step (S30) of, for each of the masks and the corresponding anatomic layers, extracting a movement feature of the heart and classifying the movement feature into one of a number of pre- determined movement features, and a global classification step (S40) using one or more predetermined thresholds and a plurality of the masks and their corresponding anatomic layers to extract therefrom and determine a global classification or evaluation for the patient as having normal wall motion or suspicious or abnormal wall motion. a myocardium segmentation step (S10) of inputting the plurality of image frames into two or more neural networks each trained to predict or determine in the image frames a portion corresponding to muscle tissue and a portion corresponding to surrounding tissue of the heart muscle, applying the trained neural networks in parallel, and fusing an output of each of the trained neural networks into a single output indicating a segmentation, for each of the plurality of image frames, between a first portion indicating muscle tissue of the heart and a second portion indicating surrounding tissue of the heart muscle, and determining a corresponding mask of muscle tissue for the first portion; a slice classification step (S20) of assigning each of the plurality of image frames in each slice, z, to an anatomic layer of the heart; a movement feature extraction step (S30) of, for each of the masks and the corresponding anatomic layers, extracting a movement feature of the heart and classifying the movement feature into one of a number of pre- determined movement features, and a global classification step (S40) using one or more predetermined thresholds and a plurality of the masks and their corresponding anatomic layers to extract therefrom and determine a global classification or evaluation for the patient as having normal wall motion or suspicious or abnormal wall motion. <EOS>