Comparative Analysis of Confusion Matrices and ROC Curves for SE-EfficientNetB0, SE-Xception and SE-Hybrid Models on the UCSD Dataset.

Comparative Analysis of Confusion Matrices and ROC Curves for SE-EfficientNetB0, SE-Xception and SE-Hybrid Models on the UCSD Dataset.

<p>Comparative Analysis of Confusion Matrices and ROC Curves for SE-EfficientNetB0, SE-Xception and SE-Hybrid Models on the UCSD Dataset.</p>

محفوظ في:
التفاصيل البيبلوغرافية
المؤلف الرئيسي: Gülcan Gencer (20691782) (author)
مؤلفون آخرون: Kerem Gencer (20691785) (author)
منشور في: 2025
الموضوعات:
Medicine
Neuroscience
Biotechnology
Cancer
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
provide high success
produced excellent results
optical coherence tomography
indicate substantial promise
duke &# 8217
aided diagnostic tools
adaptively recalibrating per
using artificial intelligence
image classification tasks
se blocks increase
model scaling strategies
model &# 8217
detecting retinal disorders
dme ), drusen
oct images using
retinal disorders
categorized using
including dme
classification performance
proposed se
hybrid se
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traditional methods
softmax algorithm
paper offers
oct images
oct datasets
manual interpretation
known approaches
findings emphasize
fewer parameters
early diagnosis
early detection
current best
combined features
choroidal neovascularization
architectures enhances
accurate detection
accuracy rates
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