Table 5. Comparison of the results of the present study with other related works involving the use of most proven, state-of-the-art models, and with mammography.

Reference	Pre-trained network employed	Dataset	Data classes	Accuracy	AUC	AUCPR
Ragab et al. (10)	Fine-tuned AlexNet	Mammography DDSM (n=1,840)	Benign and malignant breast masses	0.81	0.88	–
		Mammography CBIS-DDSM (n=5,272)		0.87	0.94	–
Xiao et al. (11)	Fine-tuned ResNet50	Breast ultrasound images (n=2,058)	Benign and malignant breast masses	0.85	0.91	–
	Fine-tuned InceptionV3			0.85	0.91	–
Byra et al. (12)	Fine-tuned VGG19 + match layer techniques	Breast ultrasound images (n=882)	Benign and malignant breast masses	0.89	0.94	–
The proposed AutoML model	–	Breast ultrasound images (n=895)	Benign and malignant breast masses	0.86	–	0.95