Abstract
We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach applies computational and mathematical formulations to extract quantitative information from biomedical images. Our study explores the longitudinal OASIS-3 dataset, which consists of 4096 brain MRI scans collected over a period of 15 years. We perform feature extraction using Pyradiomics python package that quantizes brain MRI images using different texture analysis methods. Studies indicate that Radiomics has rarely been used for analysis of brain cognition; hence, our study is also a novel effort to determine the efficiency of Radiomics features extracted from structural MRI scans for classification of brain degenerative diseases and to create awareness about Radiomics. For classification tasks, we explore various ensemble learning classification algorithms such as random forests, bagging-based ensemble classifiers, and gradient-boosted ensemble classifiers such as XGBoost and AdaBoost. Such ensemble learning classifiers have not been used for biomedical image classification. We also propose a novel texture analysis matrix, Decreasing Gray-Level Matrix or DGLM. The features extracted from this filter helped to further improve the accuracy of our decision support system. The proposed system based on XGBoost ensemble learning classifiers achieves an accuracy of 97.38%, with sensitivity 99.82% and specificity 97.01%.
Highlights
Medical image processing has travelled a long journey since the last two decades
(1) We explore the OASIS-3 dataset [23], which is a longitudinal dataset with 4096 MRI scans
This dataset gives specific details about how the CDR value changes for a subject with respect to changes in the subject’s MRI scan
Summary
Medical image processing has travelled a long journey since the last two decades. The past decade has seen the bridging of medical and information technology. It led to the development of decision support systems for early identification of various brain diseases. Studies [3,4,5] focus on specific regions of interest in brain volumes, and these are calculated from two dimensional manually traced areas. Segmentation algorithms are used to segment out gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Such volumetric studies are limited to known brain structures like hippocampus and amygdala, perirhinal, entorhinal, and parahippocampal cortex
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