Lumbar Spine Disease Detection: Enhanced CNN Model With Improved Classification Accuracy

Abstract

Back pain is an issue affecting millions of people throughout the world. Research on back pain root cause detection is immense. The lumbar spine is a lower back region of the backbone that could also be responsible for back pain. Research on issues related to lumbar spine disease (LSD) is limited. The lumbar spine is critical for the human body as it supports the weight of the body. Many diseases can affect the lumbar spine adversely. This research is directed towards the detection of LSDs using optimized feature extraction and selection phases. Furthermore, the linearity-based model is used for feature selection, selecting only the best possible features to reduce the missed classification degree. The flow of the proposed research work is divided into phases. In the first phase, data collection is performed. Data collection in the proposed work includes both real-time and benchmark magnetic resonance imaging (MRI) datasets. The MRI dataset collected from the hospital is validated by medical experts. After data collection, pre-processing is applied. This step removes the noise from the collected dataset. The pre-processing mechanism is performed using histogram equalization, median filtering, validation, and normalization. After the pre-processing phase, background subtraction and region of interest (ROI) detection are performed using the region-cut mechanism. Optimal feature extraction is achieved using a differential spider monkey optimization (SMO), and feature selection is performed using a linearity-based convolutional neural network (CNN) model. In the end, ensemble-based classification is used for disease prediction. The validation of the result is conducted through classification accuracy, specificity, sensitivity, and F-Score. The high classification accuracy of 96% is achieved with multi support vector machine (MSVM), 94% with random forest (RF), 93.5% with a decision tree (DT), and 91% with the Naïve Bayes (NB) approach, proving the validity of the proposed approach.

Keywords

• Lumbar spine
• SMO
• linearity model
• ROI
• MRI dataset