A customized ConvNeXt‐XL network with fusion of deep and handcrafted features for colposcopy image classification

Cervical cancer is the second most frequent cancer among women of all age groups worldwide. It occurs due to human papillomavirus. In the premature stages, the symptoms will not be predominant until they reach the final stage of cancer. Detection and classification of cervical cancer always demand g...

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Veröffentlicht in:	International journal of imaging systems and technology 2024-03, Vol.34 (2), p.n/a
Hauptverfasser:	Natarajan, Thendral, Devan, Lakshmi, Palayanoor Seethapathy, Ramaprabha, Balakrishnan, Senthil Kumar
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Artificial neural networks Cancer Cervical cancer Cervix Classification colposcopy ConvNeXt‐XL Customization Datasets Decision trees deep convolutional neural networks feature concatenation Human papillomavirus Image classification Machine learning Real time Support vector machines
Online-Zugang:	Volltext
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