Use of Deep Learning Models in the Diagnosis of Proptosis Through Orbital Magnetic Resonance Imaging

Uğur Kesimal; Habip Eser Akkaya; Önder Polat; Murat Sağlam

doi:10.12659/MSM.951157

18 April 2026 : Clinical Research

Use of Deep Learning Models in the Diagnosis of Proptosis Through Orbital Magnetic Resonance Imaging

Uğur Kesimal^{ABCDEF 1*}, Habip Eser Akkaya^{ABCDEF 2}, Önder Polat^{ABCDEF 1}, Murat Sağlam^{ABCDEF 1}

DOI: 10.12659/MSM.951157

Med Sci Monit 2026; 32:e951157

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Table 2 Aggregate mean classification counts of true positives (TP), true negatives (TN), false negatives (FN), and false positives (FP) per test fold.

	DenseNet121	DenseNet169	DenseNet264	ResNet50
TP	48.8	47.6	48.8	46.2
TN	50.4	49.6	47.2	47.0
FN	3.6	4.8	3.6	5.0
FP	1.6	2.4	4.8	6.2

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Use of Deep Learning Models in the Diagnosis of Proptosis Through Orbital Magnetic Resonance Imaging

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