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

TL;DR

This study compares various deep learning techniques for knee injury detection using MRI data, highlighting the effectiveness of transfer learning, data augmentation, and multi-plane approaches to achieve high accuracy.

Contribution

It introduces a comprehensive comparison of existing and new deep learning methods, emphasizing the importance of transfer learning and data augmentation for MRI-based injury detection.

Findings

Achieved 93.4% AUC with advanced architectures and data augmentation.

Transfer learning and data augmentation are key to improved performance.

Proposed flexible architectures for MRI data processing.

Abstract

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