Automated Detection of Spine Deformities: Advancing Orthopedic Care with Convolutional Neural Networks - Pratap, Deepesh; Sinha, Saran; Kumari, A. Charan; Srinivas, K. (Universitas Sanata Dharma, 2024)
This paper proposes Spine-CNN, a deep learning model for the detection of spinal deformities that can assist orthopedic doctors as a reliable tool for diagnosis. This technology promises to dramatically simplify the diagnostic process, freeing valuable time, and resources for healthcare professionals. To achieve this objective, a dataset of spine deformity X-ray images was curated from the PhysioNet database. The Spine-CNN was specially designed for detecting the spine deformity by incorporating features to leverage its ability to extract intricate features from radiographic images and by fine tuning the hyperparameters to properly train the model. Model performance was evaluated using standard metrics. Results from the Spine-CNN demonstrated promising performance in detecting spinal deformities. The model achieved an accuracy of 74%, with precision, recall, and F1-score values of 77%, 70%, and 73% respectively. Specifically, this research work introduces a Spine-CNN that underscore the potential of deep learning techniques to revolutionize diagnostic practices in orthopedic medicine, leading to improved treatment outcomes and patient care. Keywords: Computer-aided detection, Convolutional neural network, Image classification, Spine Deformation, X-ray imaging