CerviLearnNet: Advancing cervical cancer diagnosis with reinforcement learning-enhanced convolutional networks

Muksimova, Shakhnoza; Umirzakova, Sabina; Kang, Seokwhan; Cho, Young Im

doi:10.1016/j.heliyon.2024.e29913

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e29913

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CerviLearnNet: Advancing cervical cancer diagnosis with reinforcement learning-enhanced convolutional networks

Shakhnoza Muksimova,

Sabina Umirzakova,

Seokwhan Kang

et al.

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2024

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Cited by 3 publications

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Enhancing Nanomaterial-Based Optical Spectroscopic Detection of Cancer through Machine Learning

Sahli,

Kenry

2024

ACS Materials Lett.

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Optical spectroscopic techniques relying on light−matter interactions, such as Raman scattering, fluorescence, and infrared absorbance spectroscopy, offer numerous advantages to complement existing cancer detection methods. By combining these spectroscopic techniques with rationally engineered nanomaterials, cancer cells and tissues can be more specifically targeted, and the readout signals can be substantially enhanced. Further integration of machine learning with its potential to identify subtle malignancy indicators may significantly improve the capability of nanomaterial-enabled optical spectroscopy to delineate cancer more precisely. As such, the synergistic integration of optical spectroscopy, nanomaterials, and machine learning may provide unique opportunities for the development of more selective, sensitive, and accurate cancer diagnostic technologies, which can be leveraged to optimize therapeutic strategies and minimize unnecessary interventions to ultimately enhance patient survival outcomes. This Perspective describes numerous strategies incorporating optical spectroscopy, nanomaterials, and machine learning to improve cancer detection and summarizes our outlook on the current landscape and potential future directions of this emerging field.

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Enhancing Nanomaterial-Based Optical Spectroscopic Detection of Cancer through Machine Learning

Sahli,

Kenry

2024

ACS Materials Lett.

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Novelty Classification Model Use in Reinforcement Learning for Cervical Cancer

Muksimova,

Umirzakova,

Shoraimov

et al. 2024

Cancers

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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.

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Optimized YOLOv5 Architecture for Superior Kidney Stone Detection in CT Scans

Abdimurotovich,

Cho

2024

Electronics

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The early and accurate detection of kidney stones is crucial for effective treatment and improved patient outcomes. This paper proposes a novel modification of the YOLOv5 model, specifically tailored for detecting kidney stones in CT images. Our approach integrates the squeeze-and-excitation (SE) block within the C3 block of the YOLOv5m architecture, thereby enhancing the ability of the model to recalibrate channel-wise dependencies and capture intricate feature relationships. This modification leads to significant improvements in the detection accuracy and reliability. Extensive experiments were conducted to evaluate the performance of the proposed model against standard YOLOv5 variants (nano-sized, small, and medium-sized). The results demonstrate that our model achieves superior performance metrics, including higher precision, recall, and mean average precision (mAP), while maintaining a balanced inference speed and model size suitable for real-time applications. The proposed methodology incorporates advanced noise reduction and data augmentation techniques to ensure the preservation of critical features and enhance the robustness of the training dataset. Additionally, a novel color-coding scheme for bounding boxes improves the clarity and differentiation of the detected stones, facilitating better analysis and understanding of the detection results. Our comprehensive evaluation using essential metrics, such as precision, recall, mAP, and intersection over union (IoU), underscores the efficacy of the proposed model for detecting kidney stones. The modified YOLOv5 model offers a robust, accurate, and efficient solution for medical imaging applications and represents a significant advancement in computer-aided diagnosis and kidney stone detection.

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CerviLearnNet: Advancing cervical cancer diagnosis with reinforcement learning-enhanced convolutional networks

Cited by 3 publications

References 37 publications

Enhancing Nanomaterial-Based Optical Spectroscopic Detection of Cancer through Machine Learning

Enhancing Nanomaterial-Based Optical Spectroscopic Detection of Cancer through Machine Learning

Novelty Classification Model Use in Reinforcement Learning for Cervical Cancer

Optimized YOLOv5 Architecture for Superior Kidney Stone Detection in CT Scans

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