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%.

Brain atrophy is the degradation of brain cells and tissues to the extent that it is clearly indicative during Mini-Mental State Exam test and other psychological analysis. It is an alarming state of the human brain that progressively results in Alzheimer disease which is not curable. But timely detection of brain atrophy can help millions of people before they reach the state of Alzheimer. In this study we analyzed the longitudinal structural MRI of older adults in the age group of 42 to 96 of OASIS 3 Open Access Database. The nth slice of one subject does not match with the nth slice of another subject because the head position under the magnetic field is not synchronized. As a radiologist analyzes the MRI image data slice wise so our system also compares the MRI images slice wise, we deduced a method of slice by slice registration by driving mid slice location in each MRI image so that slices from different MRI images can be compared with least error. Machine learning is the technique which helps to exploit the information available in abundance of data and it can detect patterns in data which can give indication and detection of particular events and states. Each slice of MRI analyzed using simple statistical determinants and Gray level Co-Occurrence Matrix based statistical texture features from whole brain MRI images. The study explored varied classifiers Support Vector Machine, Random Forest, K-nearest neighbor, Naive Bayes, AdaBoost and Bagging Classifier methods to predict how normal brain atrophy differs from brain atrophy causing cognitive impairment. Different hyper parameters of classifiers tuned to get the best results. The study indicates Support Vector Machine and AdaBoost the most promising classifier to be used for automatic medical image analysis and early detection of brain diseases. The AdaBoost gives accuracy of 96.76% with specificity 95.87% and sensitivity 87.37% and receiving operating curve accuracy 96.3%. The SVM gives accuracy of 96% with 92% specificity and 87% sensitivity and receiving operating curve accuracy 95.05%.

No abstract

No abstract