Classification of Spine Image from MRI Image Using Convolutional Neural Network

Abstract

The spine tumor is a fast-growing abnormal cell in the spinal canal or vertebrae of the spine, it affected many people. Thousands of researchers have focused on this disease for better understanding of tumor classification to provide more effective treatment to the patients. The main objective of this paper is to form a methodology for classification of spine image. We proposed an efficient and effective method that helpful for classifying the spine image and identified tumor region without any human assistance. Basically, Contrast Limited Adaptive Histogram Equalization used to improve the contrast of spine images and to eliminate the effect of unwanted noise. The proposed methodology will classify spine images as Normal or Abnormal using Convolutional Neural Network (CNN) model algorithm. The CNN model can classify spine image as Normal or Abnormal with 99.4% Accuracy, 94.5% Sensitivity, 95.6% Precision, and 99.9% specificity. Compared with the previous existing methods, our proposed solution achieved the highest performance in terms of classification based on the spine dataset. From the experimental results performed on the different images, it is clear that the analysis for the spine tumor detection is fast and accurate when compared with the manual detection performed by radiologists or clinical experts, So, anyone can easily identify the tumor affected area also determine abnormal images.