Novelty Classification Model Use in Reinforcement Learning for Cervical Cancer.

Simple Summary: Cervical cancer significantly impacts women's health worldwide, necessitating early detection for improved treatment outcomes. Traditional screening methods, such as Pap smears, however, are limited in accuracy and rely on subjective expert analysis. This study introduces RL-CancerNet, a novel artificial intelligence model that enhances cervical cancer screening by analyzing cytology images with advanced computational techniques. RL-CancerNet integrates EfficientNetV2 for detailed image analysis, Vision Transformers for contextual understanding, and Reinforcement Learning to focus on rare but critical features indicative of early-stage cancer. When tested on standard datasets, our model achieved a remarkable 99.7% accuracy, surpassing existing methods. This breakthrough suggests that RL-CancerNet can make cervical cancer detection more reliable and accessible, with potential applications across other medical imaging domains. Purpose: Cervical cancer significantly impacts global health, where early detection is piv- otal for improving patient outcomes. This study aims to enhance the accuracy of cervical cancer diagnosis by addressing class imbalance through a novel hybrid deep learning model. Methods: The proposed model, RL-CancerNet, integrates EfficientNetV2 and Vision Transformers (ViTs) within a Reinforcement Learning (RL) framework. EfficientNetV2 extracts local features from cervical cytology images to capture fine-grained details, while ViTs analyze these features to recognize global dependencies across image patches. To address class imbalance, an RL agent dynamically adjusts the focus towards minority classes, thus reducing the common bias towards majority classes in medical image classification. Additionally, a Supporter Module incorporating Conv3D and BiLSTM layers with an attention mechanism enhances contextual learning. Results: RL-CancerNet was evaluated on the benchmark cervical cytology datasets Herlev and SipaKMeD, achieving an exceptional accuracy of 99.7%. This performance surpasses several state-of-the-art models, demonstrating the model's effectiveness in identifying subtle diagnostic features in complex backgrounds. Conclusions: The integration of CNNs, ViTs, and RL into RL-CancerNet significantly improves the diagnostic accuracy of cervical cancer screenings. This model not only advances the field of automated medical screening but also provides a scalable framework adaptable to other medical imaging tasks, potentially enhancing diagnostic processes across various medical domains. [ABSTRACT FROM AUTHOR]