Enhancing Cervical Cancer Classification in Pap Smears

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A comprehensive study addressing the challenges of cervical cancer detection, emphasising the role of Pap smear diagnostics and advanced deep learning methods. It reviews prior classification models, highlights the efficiency of EfficientNet architectures, and presents a methodology involving data preparation, ensemble learning, and performance metrics. Key findings include a 99.25% accuracy rate, showcasing advancements over previous techniques. The study underscores the significance of...