Anterior Cruciate Ligament (ACL) Tear Detection Using Hybrid CNN Transformer

Classifying medical images to detect ACL (Anterior Cruciate Ligament) tears is the medical image classification task, which requires significant importance to avoid misdiagnosis. However, this also poses some difficulties due to differences in formats of medical images and standard images. While Convolutional neural networks (CNN) and transformers are among the types of models that have been used for such tasks, they both have their weaknesses. For instance, CNNs have a tendency of extracting local features, hence have problems in understanding global context but on the other hand, transformers excel in understanding global information even if it implies losing some details in localization. Because of this, we introduce the MambaConvT (Mamba Convolutional Transformer) model that employs a hybrid strategy that combines CNNs and transformers giving room for the advantages of both. Firstly, MambaConvT utilizes multi-core convolutional networks to achieve higher extraction capability of the ACL tear specific local features from structural MR (Magnetic Resonance) images. Next, it uses a selective scanning module to implement depth separable convolution using Selective Scanning Module (SS2D) to achieve a larger distance of the receptive field therefore increasing the chances of getting important long-distance connections and essential fine details for the tear detection process. A hybrid model is utilized that integrates these hybrid features and after global feature modeling, attempts to enhance both local and global information representation. MambaConvT outperforms the current major classification models, according to a robust performance evaluation conducted on four public and two protected datasets. It also yields the best detection scores, taking into account all evaluation metrics, such as accuracy, recall, precision, F1 score, and area under the curve, for the identification of ACL tears in medical imaging.