Designing, Performing and Evaluating a Multilayer Classification Pipeline of Neural Network Architectures, for Automated Diagnosis of Critical Hip Conditions, in Plain Anteroposterior Pelvic X-rays

Abstract

The entrance of artificial intelligence (AI) in radiological diagnostics has gained considerable attention, particularly through the utilization of deep learning architectures for the analysis of medical images. This study proposes a multi-stage convolutional neural network (CNN) pipeline intended for classifying plain anteroposterior pelvic X-rays in twelve different categories, including normal, traumatic, degenerative, and postoperative hip conditions. The diagnostic procedure incorporates five sequential stages, with ResNet50 employed for the initial classification of hip side and VGG16 utilized for the subsequent and more complex diagnostic tasks. A dataset of 812 unique, diligently anonymized X-rays, supplemented by synthetic images that were generated by a Conditional Generative Adversarial Network (GAN), was employed to train and validate the model. The system demonstrated high accuracy in early-stage binary classifications, such as differentiating normal from abnormal hip X-rays and differentiating between operated and non-operated hip X-rays. However, performance declined in multi-class classification tasks, particularly in the identification of a specific type of hip fracture or surgical technique. These results highlight the potential of AI-driven diagnostic tools in augmenting the radiological decision-making process by improving diagnostic sensitivity, specificity, and time efficiency. The goal of this article is not to introduce a highly accurate diagnostic platform, but to investigate and present the workflow of an automated, comprehensive, multistep diagnostic procedure, capable of utilizing the existing or upcoming radiological studies concerning the binary distinction between two specific medical conditions. The findings emphasize the need for further refinement, particularly in multi-class classification, to enhance the overall performance of such systems, while human expertise remains irreplaceable for final clinical decision-making.

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