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This work presents a comparative study of existing and new techniques todetect knee injuries by leveraging Stanford's MRNet Dataset. All approaches arebased on deep learning and we explore the comparative performances of transferlearning and a deep residual network trained from scratch. We also exploit somecharacteristics of Magnetic Resonance Imaging (MRI) data by, for example, usinga fixed number of slices or 2D images from each of the axial, coronal andsagittal planes as well as combining the three planes into one multi-planenetwork. Overall we achieved a performance of 93.4% AUC on the validation databy using the more recent deep learning architectures and data augmentationstrategies. More flexible architectures are also proposed that might help withthe development and training of models that process MRIs. We found thattransfer learning and a carefully tuned data augmentation strategy were thecrucial factors in determining best performance.

A Comparative Study of Existing and New Deep Learning Methods for Detecting Knee Injuries using the MRNet Dataset