SynthraXCoreNet: An Interpretable, Well-Calibrated Six-CNN Ensemble for Dermoscopic Skin-Lesion Classification

Accurate skin lesion classification is hard because classes can look similar, datasets are imbalanced, and devices and domains vary. We introduce SynthraXCoreNet, a six CNN ensemble with ResNet50V2, ResNet101V2, ResNet152V2, DenseNet201, NASNetLarge, and Xception. Each backbone is trained per datase...

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主要な著者:	Muhammad Adeel Ajmal Khan, Madiha Tahir, Syed Ali Irtaza, Muhammad Rizwan Mughal, Muhammad Nadeem Yousaf
フォーマット:	Artigo
言語:	Inglês
出版事項:	IEEE 2026-01-01
シリーズ:	IEEE Access
主題:	Dermatology dermoscopy skin cancer skin lesion classification image classification deep learning
オンライン･アクセス:	https://ieeexplore.ieee.org/document/11284879/
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