Revolutionizing Acne Diagnosis With Hybrid Deep Learning Model Integrating CBAM, and Capsule Network

Acne is the eighth most prevalent global health concern and affects around 9.4% of the global population. The accurate classification of diverse acne categories is crucial for promptly creating efficient treatment plans. Although various deep learning (DL) models are available in the literature, ach...

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Veröffentlicht in:	IEEE access 2024, Vol.12, p.82867-82879
Hauptverfasser:	Krishna Veni, Paluri, Gupta, Ashish
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Acne images capsule network Classification CNN convolutional block attention module (CBAM) Convolutional neural networks Data augmentation Datasets Deep learning Diseases Feature extraction Machine learning medical image processing Public health Skin Support vector machines Task analysis VGG16
Online-Zugang:	Volltext
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