[Retracted] Keratoconus Classification with Convolutional Neural Networks Using Segmentation and Index Quantification of Eye Topography Images by Particle Swarm Optimisation

Table 1

Machine learning techniques applied for keratoconus classification in the literature.

Authors	Classification	Network	Accuracy	Sensitivity	Specificity
Accardo et al.	Normal and keratoconus	BPN	98	93.3	98.6
Souza et al.	Normal and keratoconus	SVM	—	100	75
		MLP	—	100	75
		RBFNN	—	98	75
Toutounchian et al.	Normal, mild keratoconus, and keratoconus	MLP	77.6	—	—
		SVM	72	—	—
		DT	84	—	—
		RBFNN	71.2	—	—
Arbelaez et al.	Normal, abnormal, subclinical, and keratoconus	SVM	95.275	87.6	96.9
Hidalgo et al.	Astigmatism, forme fruste keratoconus, keratoconus, normal, and postrefractive surgery	SVM	88.8	77.22	97.02
Lavric et al.	Keratoconus, forme fruste keratoconus, and normal	QSVM	93	—	—
Santos et al.	Normal and keratoconus	CorneaNet	99.56
Kamiya et al.	Normal and keratoconus 4 gradings	ResNet-18	99.1	100	98.4
Shi et al.	Normal, keratoconus, and subclinical	NN	93
Kuo et al.	Normal and keratoconus	VGG16	93.1	91.7	94.4
		InceptionV3	93.1	91.7	94.4
		ResNet152	95.8	94.4	97.2
Lavric et al.	5 classes as normal, forme fruste, keratoconus II, keratoconus III, and keratoconus IV	SVM	AUC 0.88
	3 classes as normal, forme fruste, and keratoconus	SVM	AUC 0.96
	Normal and keratoconus	SVM	AUC 0.99
Cao et al.	Normal and keratoconus	SVM	88.8