Interpretable 3D Multi-Modal Residual Convolutional Neural Network for
  Mild Traumatic Brain Injury Diagnosis
Hanem Ellethy Viktor Vegh Shekhar S. Chandra
Mild Traumatic Brain Injury (mTBI) is a significant public health challenge
due to its high prevalence and potential for long-term health effects. Despite
Computed Tomography (CT) being the standard diagnostic tool for mTBI, it often
yields normal results in mTBI patients despite symptomatic evidence. This fact
underscores the complexity of accurate diagnosis. In this study, we introduce
an interpretable 3D Multi-Modal Residual Convolutional Neural Network (MRCNN)
for mTBI diagnostic model enhanced with Occlusion Sensitivity Maps (OSM). Our
MRCNN model exhibits promising performance in mTBI diagnosis, demonstrating an
average accuracy of 82.4%, sensitivity of 82.6%, and specificity of 81.6%, as
validated by a five-fold cross-validation process. Notably, in comparison to
the CT-based Residual Convolutional Neural Network (RCNN) model, the MRCNN
shows an improvement of 4.4% in specificity and 9.0% in accuracy. We show that
the OSM offers superior data-driven insights into CT images compared to the
Grad-CAM approach. These results highlight the efficacy of the proposed
multi-modal model in enhancing the diagnostic precision of mTBI.