Inv-AlxVGGNets: Cervical Spine Disease Classification Using Concatenated Involutional Neural Networks With Residual Net

Cervical spine diseases, encompassing conditions like spondylolisthesis, disc degeneration, and cervical spinal stenosis, stand as significant contributors to global disability. Precise classification of these conditions is paramount for effective medical diagnosis. This paper introduces an innovative methodology aimed at addressing the limitations of traditional convolutional neural networks (CNNs) and pretrained models in this domain. We propose a novel approach dubbed Inv-AlxVGGNets, which leverages concatenated pretrained architectures AlexNet and VGG, augmented by involutional neural networks and residual layers. Unlike conventional CNNs that are location-specific and channel-agnostic, involutional neural networks offer enhanced Adaptability to diverse visual patterns in medical images. Focusing on a four-class cervical spine disease classification task utilizing MRI images, our study evaluates the performance of Inv-AlxVGGNets (AlexNet with INN and VGG with residual layer models) as well as machine learning algorithms. Our findings demonstrate superior performance in terms of accuracy, precision, recall, and AUC ROC values. Notably, Inv-AlxVGGNets achieves an impressive 98.73% accuracy on the testing set and 99.78% on the training set, underscoring its potential for precise cervical spinal disease classification. In a comparative analysis, we highlight that conventional CNNs entail over 133 million parameters, whereas Inv-AlxVGGNets require less than 8 million parameters, rendering them more efficient and resource-friendly. This reduced parameter count is particularly advantageous in resource-constrained scenarios, where computational resources and datasets may be limited. The promising results position Inv-AlxVGGNets as a valuable tool for precise cervical spine disease classification, offering implications for enhancing patient care in resource-constrained settings.