Enhancing Cervical Cancer Classification: Through a Hybrid Deep Learning Approach Integrating DenseNet201 and InceptionV3

This paper proposes a hybrid deep learning model integrating DenseNet201 and InceptionV3 to address the challenges in achieving accurate and reliable cervical cancer classification. Current models often exhibit limitations in balancing precision and recall, which are critical for dependable clinical...

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Egile Nagusiak:	Abhiram Sharma, R. Parvathi
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	IEEE 2025-01-01
Saila:	IEEE Access
Gaiak:	Cervical cancer classification deep learning DenseNet201 feature fusion fine tuning hybrid model
Sarrera elektronikoa:	https://ieeexplore.ieee.org/document/10835083/
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