MRI-Based Spinal Condition Classification Using Improved 3DRMAU-Net-DCSE and Ensembled Deep Learning with Optimization Techniques

Spinal cord diseases are prevalent worldwide and affect people of all age groups. Since manual diagnosis is time consuming, aids of Artificial Intelligence (AI) are involved in disease diagnosis. Several approaches have been developed for spinal condition classification. However, complexities occur due to computational overhead or misclassifications. Intending to introduce a novel disease diagnosis model, this paper presented a NeuroFusionNet to accurately detect various spinal cord diseases. For effective classifier performance, significant preprocessing is mandatory. For this reason, this research incorporates median filtering, Modified Local Contrast Mapping (M-LCM), and Contrast Limited Adaptive Histogram Equalization (CLAHE) to prepare the raw data for further processing. Besides, partitioning the spinal cord from the Magnetic Resonance Imaging (MRI) is necessary to enhance visualization and accurate diagnosis using proposed 3D Residual Multi-Scale Attention U-Net with Dilated Convolution and Squeeze-and-Excitation Networks (3DRMAU-Net-DCSE). Significant features are then extracted via Convolutional Autoencoder (CAE) and optimal features are selected via proposed Advanced Dwarf Mongoose Fox optimization (ADMFO). Finally, an innovative deep learning (DL)-based NeuroFusionNet is designed using Vision Transformer (ViT), Temporal Convolutional Neural Network (CNN)+, and attention-based Capsule Network (CapsNet) to enable reliable diagnosis and decision-making in spinal health conditions effectively. To evaluate the performance of the suggested model, a comparative study is conducted with benchmark models for several performance metrics such as accuracy, recall, and F1-score. On the spine MRI dataset, the stated DL model achieved an overall accuracy of 98.88%, sensitivity of 98.95%, and F1-score of 98.95% and proved its significance over other models.