SPINE-EDL NET: ENSEMBLE APPROACH FOR CERVICAL SPINAL FRACTURE DETECTION - Tahira Kanwal; Rabbia Mahum; Syed Aun Irtaza; Usama Irshad; Romail Khan; Faisal Shafique Butt (Journal of Population Therapeutics and Clinical Pharmacology, 2024)
Deep learning algorithms have shown significant potential for early disease identification and prevention, becoming increasingly popular in recent years. Cervical spine injuries require immediate diagnosis to ensure adequate treatment. Numerous methods have been projected; but, they repeatedly lack accurateness in detecting minor fractures and may surge the false positive rate due to the limitations of single network-based classifiers. Moreover, the shortage of publicly obtainable spine data makes automated cervical spine fracture detection ominously more challenging to attain. To address these issues, we suggest a new and vigorous method called the Spine-Ensemble Deep Learning Network (Spine-EDLNet). Ensemble learning subsidises a vital role in extracting powerful features from image data. Our model harnesses the strengths of three pretrained deep learning networks: EfficientNetV2, InceptionNetV3, and VGG16, united with a majority voting mechanism. Tailored layers are combined into each model to improve fracture classification. Besides, comprehensive data augmentation and preprocessing practices are applied to the dataset before training, successfully overcoming the dataset obtainability challenge. Investigational results validate that Spine-EDLNet outperforms previous models, attaining a maximum accuracy of 99.6%. This methodology purposes to optimize diagnostic accuracy, robustness, and generalization.