  Mild Traumatic Brain Injury (mTBI) is a common and challenging condition to
diagnose accurately. Timely and precise diagnosis is essential for effective
treatment and improved patient outcomes. Traditional diagnostic methods for
mTBI often have limitations in terms of accuracy and sensitivity. In this
study, we introduce an innovative approach to enhance mTBI diagnosis using 3D
Computed Tomography (CT) images and a metric learning technique trained with
triplet loss. To address these challenges, we propose a Residual Triplet
Convolutional Neural Network (RTCNN) model to distinguish between mTBI cases
and healthy ones by embedding 3D CT scans into a feature space. The triplet
loss function maximizes the margin between similar and dissimilar image pairs,
optimizing feature representations. This facilitates better context placement
of individual cases, aids informed decision-making, and has the potential to
improve patient outcomes. Our RTCNN model shows promising performance in mTBI
diagnosis, achieving an average accuracy of 94.3%, a sensitivity of 94.1%, and
a specificity of 95.2%, as confirmed through a five-fold cross-validation.
Importantly, when compared to the conventional Residual Convolutional Neural
Network (RCNN) model, the RTCNN exhibits a significant improvement, showcasing
a remarkable 22.5% increase in specificity, a notable 16.2% boost in accuracy,
and an 11.3% enhancement in sensitivity. Moreover, RTCNN requires lower memory
resources, making it not only highly effective but also resource-efficient in
minimizing false positives while maximizing its diagnostic accuracy in
distinguishing normal CT scans from mTBI cases. The quantitative performance
metrics provided and utilization of occlusion sensitivity maps to visually
explain the model's decision-making process further enhance the
interpretability and transparency of our approach.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning