research Scalp Disease Detection Using Deep Learning: A Hybrid Ensemble Approach with EfficientNetB0, DenseNet121 and MobileNetV2
The hybrid ensemble model improves scalp disease detection accuracy and consistency.
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5 / 33 resultsresearch Classification of Visually Similar Scalp Diseases using Deep Learning: A Hybrid CNN-VIT Approach with Cross-Attention Fusion
ScalpViT accurately diagnoses similar-looking scalp diseases with 94.3% accuracy.
research Analyse trichoskopischer Bilder mit tiefen neuronalen Netzen zur Diagnose und Aktivitätsbewertung von Alopecia areata – eine retrospektive Studie
Deep-learning models can effectively diagnose and assess Alopecia areata using scalp images.
research Classification of Visually Similar Scalp Diseases using Deep Learning: A Hybrid CNN-VIT Approach with Cross-Attention Fusion
ScalpViT accurately diagnoses similar scalp diseases with 94.3% accuracy.
research Classification of Visually Similar Scalp Diseases using Deep Learning: A Hybrid CNN-VIT Approach with Cross-Attention Fusion
ScalpViT accurately diagnoses similar-looking scalp diseases with 94.3% accuracy.
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