CerviLearnNet: Advancing Cervical Cancer Diagnosis with Reinforcement Learning-Enhanced Convolutional Networks **Keywords:** Reinforcement learning, Convolutional neural networks, Cervical cancer, Machine learning, Image classification **Abstract:** Cervical cancer is a significant health issue, particularly in women, with high mortality rates. Early detection and treatment of precancerous lesions are crucial for combating this disease. This study proposes a novel architecture called the Reinforcement Learning Cancer Network (RL-CancerNet) to enhance cervical cancer diagnosis using reinforcement learning and convolutional neural networks (CNNs). The proposed method combines a modified EfficientNetV2 model with auxiliary modules and a special residual block to capture contextual interactions between object classes and support the object reference strategy. The model was trained and tested on two public datasets, SipaKMeD and Herley, to evaluate its performance. The results show that the proposed method achieves higher accuracy, precision, recall, and F1 score compared to state-of-the-art (SOTA) classification methods, demonstrating its effectiveness in cervical cancer diagnosis. **Introduction:** Cervical cancer is the fourth most common cancer among women, causing significant morbidity and mortality worldwide. Early detection and treatment are essential for improving outcomes. Computer-aided diagnosis (CADx) systems, particularly those using CNNs, have shown promise in enhancing the accuracy of cervical cancer screening. However, traditional CNNs have limitations in capturing long-range relationships in images. To address this, the study integrates reinforcement learning (RL) with CNNs to improve the model's decision-making process. **Proposed Method:** The proposed method involves a meta-learning ensemble of CNNs, incorporating a reinforcement learning algorithm to automate the cancer categorization process. The model uses a deep Q-learning network (DQN) to train the classifier. The supporter blocks, consisting of convolution layers and bidirectional long short-term memory (BiLSTM) layers, enhance the model's ability to capture contextual information. The RL-CancerNet model demonstrates a 0.79% improvement in accuracy compared to the baseline EfficientNetV2 model, achieving a total accuracy of 99.32%. **Dataset and Metrics:** The study uses two public datasets, SipaKMeD and Herley, to evaluate the model's performance. Data augmentation techniques are applied to increase the variability and complexity of the training data. Six performance metrics—accuracy, recall, precision, F-measure, specificity, and G-means—are used to assess the model's effectiveness. **Experimental Results:** The proposed method outperforms several state-of-the-art models, achieving the highest accuracy (99.70%), precision (99.36%), recall (99.90%), and F1 score (99.72%). The model also shows superior performance in handling class imbalance, with a high sensitivity of 99.50% and aCerviLearnNet: Advancing Cervical Cancer Diagnosis with Reinforcement Learning-Enhanced Convolutional Networks **Keywords:** Reinforcement learning, Convolutional neural networks, Cervical cancer, Machine learning, Image classification **Abstract:** Cervical cancer is a significant health issue, particularly in women, with high mortality rates. Early detection and treatment of precancerous lesions are crucial for combating this disease. This study proposes a novel architecture called the Reinforcement Learning Cancer Network (RL-CancerNet) to enhance cervical cancer diagnosis using reinforcement learning and convolutional neural networks (CNNs). The proposed method combines a modified EfficientNetV2 model with auxiliary modules and a special residual block to capture contextual interactions between object classes and support the object reference strategy. The model was trained and tested on two public datasets, SipaKMeD and Herley, to evaluate its performance. The results show that the proposed method achieves higher accuracy, precision, recall, and F1 score compared to state-of-the-art (SOTA) classification methods, demonstrating its effectiveness in cervical cancer diagnosis. **Introduction:** Cervical cancer is the fourth most common cancer among women, causing significant morbidity and mortality worldwide. Early detection and treatment are essential for improving outcomes. Computer-aided diagnosis (CADx) systems, particularly those using CNNs, have shown promise in enhancing the accuracy of cervical cancer screening. However, traditional CNNs have limitations in capturing long-range relationships in images. To address this, the study integrates reinforcement learning (RL) with CNNs to improve the model's decision-making process. **Proposed Method:** The proposed method involves a meta-learning ensemble of CNNs, incorporating a reinforcement learning algorithm to automate the cancer categorization process. The model uses a deep Q-learning network (DQN) to train the classifier. The supporter blocks, consisting of convolution layers and bidirectional long short-term memory (BiLSTM) layers, enhance the model's ability to capture contextual information. The RL-CancerNet model demonstrates a 0.79% improvement in accuracy compared to the baseline EfficientNetV2 model, achieving a total accuracy of 99.32%. **Dataset and Metrics:** The study uses two public datasets, SipaKMeD and Herley, to evaluate the model's performance. Data augmentation techniques are applied to increase the variability and complexity of the training data. Six performance metrics—accuracy, recall, precision, F-measure, specificity, and G-means—are used to assess the model's effectiveness. **Experimental Results:** The proposed method outperforms several state-of-the-art models, achieving the highest accuracy (99.70%), precision (99.36%), recall (99.90%), and F1 score (99.72%). The model also shows superior performance in handling class imbalance, with a high sensitivity of 99.50% and a