A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers

Table 2

Comparison with similar studies.

Research study	Year	Dataset	Brain area	Classifier	Accuracy
Ahmad Chaddad [32]	2018	OASIS-1	Hippocampus; Amygdala	Random forest; Random forest	84.09%
				CNN	92.5%
Feng Feng [33]	2018	Local hospital data	Hippocampus	SVM	86.75%
Yupeng Li and Jiehui Jiang [34]	2019	Local hospital data	Hippocampus	SVM	91.5%
Kun Zhao [35]	Jan 2020	ADNI	Hippocampus	SVM	88.21%
Tao-Ran Li [36]	Dec 2020	ADNI	Right posterior and left superior cingulate Gyrus	SVM	95.9%
Current study proposed by us	2021	OASIS-3	Whole brain	Ensemble classifiers
				XGBoost	97.38%
				AdaBoost	97.21%
				Bagging	87.56%
				Random forest	87.72%