Table 2.

Previous Literature Investigating the Detection of KCN From Corneal Topographic Images

Study	KCN Classes	Device Used	Dataset/Number of Maps	Evaluation Method	Network Used	Accuracy
Kamiya et al.19	Normal and 4 grades of KCN	Tomey CASIA	543 cases/6 maps	Fivefold CV	ResNet-18	99%
Kuo et al.20	Normal, KCN	Tomey TMS-4 Corneal Topographer	354 cases/1 map	Training, testing, and subclinical testing	VGG16 InceptionV3 ResNet152	93.1% 93.1%95.8%
Lavric and Valentin21	Normal, KCN	Synthetic maps	SyntEyes and SyntEyes KTC models58/1 map	Training, validation, and testing	KeratoDetect	99.3%
Zeboulon et al.22	Normal/KCN and history of refractive surgery	Bausch + Lomb Orbscan	3000 cases/4 maps	Tenfold CV	CNN	99.3%
Al-Timemy et al.23	Normal, KCN	OCULUS Pentacam	534 cases/4 maps	Training, validation, and testing	EDTL with AlexNet and product fusion	98.3%
Current study	Normal, KCN, suspected KCN	OCULUS Pentacam	692 eyes/7 maps	Training, validation, and independent testing	EfficientNet-b0 DL with SVM	Two-class, 98% Three-class, 81.6%

CV, cross-validation; EDTL, ensemble deep transfer learning.