Table 2.

Confusion matrix to evaluate accuracy of different methods.

Prediction used different methods
	Detected faces	Facial features			Frontalized faces
Normal	Normal	Acromegaly	Normal	Acromegaly	Normal	Acromegaly
Acromegaly	Acromegaly	Normal	Acromegaly	Normal	Acromegaly	Normal
LM	102	26	64	64	102	26
	86	28	103	11	100	14
KNN	102	26	113	15	119	9
	97	17	103	11	101	13
SVM	108	20	123	5	115	13
	91	23	98	16	88	26
RT	102	26	101	27	111	17
	97	17	98	16	103	11
CNN	111	17	–	–	123	5
	104	10	–	–	104	10
Ensemble MLs	–	–	–	–	123	5
	–	–	–	–	109	5
Specialists in pituitary disease (average)a	–	–	–	–	118	10
	–	–	–	–	83	31
Primary care doctors (average)a	–	–	–	–	109	19
	–	–	–	–	78	36

Confusion matrix in this table shows the details about the predictions and observations, which can evaluate the accuracy of classification based on different methods.

^aThe values for doctors are calculated based on doctors' diagnosis records on original facial images.