Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms

Table 1

Comparison of classification methods in biomedical image based on the literature [32, 46].

	Decision trees	Neural networks	Naïve bayes	KNN	SVM	Rule-learning
Accuracy	∗∗	∗∗∗	∗	∗∗	∗∗∗∗	∗∗
Speed of classification	∗∗∗∗	∗∗∗∗	∗∗∗∗	∗	∗∗∗∗	∗∗∗∗
Tolerance to redundant attributes	∗∗	∗∗	∗	∗∗	∗∗∗	∗∗
Speed of learning	∗∗∗	∗	∗∗∗∗	∗∗∗∗	∗	∗∗
Tolerance to missing values	∗∗∗	∗	∗∗∗∗	∗	∗∗	∗∗
Tolerance to highly interdependent attributes	∗∗	∗∗∗	∗	∗	∗∗∗	∗∗
Dealing with discrete/binary/continues attributes	∗∗∗∗	∗∗∗ (not discrete)	∗∗∗ (not continuous)	∗∗∗ (not directly discrete)	∗∗ (Not discrete)	∗∗∗ (not directly discrete)
Tolerance to noise	∗∗	∗∗	∗∗∗	∗	∗∗	∗
Dealing with a danger of overfitting	∗∗	∗	∗∗∗	∗∗∗	∗∗	∗∗
Attempts for incremental learning	∗∗	∗∗∗	∗∗∗∗	∗∗∗∗	∗∗	∗

^∗∗∗∗Very good. ^∗∗∗Good. ^∗∗Fairly Good. ^∗Bad.