Intervertebral Cervical Disc Intensity (IVCDI) Detection and Classification on MRI Scans Using Deep Learning Methods

Abstract

Radiologists manually interpret magnetic resonance imaging (MRI) scans for the detection of intervertebral cervical disc degeneration, which are often obtained in a primary care or emergency hospital context. The ability of computer models to work with pathological findings and aid in the first interpretation of medical imaging tests is widely acknowledged. Deep learning methods, which are commonly employed today in the diagnosis or detection of many diseases, show great promise in this area. For the detection and segmentation of intervertebral cervical disc intensity, we propose a Mask-RCNN-based deep learning algorithm in this study. The provided approach begins by creating an original dataset using MRI scans that were collected from Yozgat Bozok University. The senior radiologist labels the data, and three classes of intensity are chosen for the classification (low, intermediate, and high). Two alternative network backbones are used in the study, and as a consequence of the training for the Mask R-CNN algorithm, 98.14% and 96.72% mean average precision (mAP) values are obtained with the ResNet50 and ResNet101 architectures, respectively. Utilizing the five-fold cross-validation approach, the study is conducted. This study also applied the Faster R-CNN method, achieving a mAP value of 85.2%. According to the author's knowledge, no study has yet been conducted to apply deep learning algorithms to detect intervertebral cervical disc intensity in a patient population with cervical intervertebral disc degeneration. By ensuring accurate MRI image interpretation and effectively supplying supplementary diagnostic information to provide accuracy and consistency in radiological diagnosis, the proposed method is proving to be a highly useful tool for radiologists.