Fractional rider gradient descent applied U-Net based segmentation with optimal deep maxout network for lung cancer classification using histopathological images

Patra, Radhanath; Khuntia, Bonomali; Panda, Dhruba C.

doi:10.1007/s42600-022-00211-5

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Patra et al [122] proposed a lung cancer histopathological image classification system using fractional rider gradient descent-based U-Net segmentation. The deep neural network, trained with dolphin-based HGSO (DHGSO), achieved superior performance with 93.08% accuracy, 94.81% sensitivity, and 94.59% specificity.…”

Section: B Hybridized Versions Of Hgsomentioning

confidence: 99%

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

IEEE Access

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The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by Henry's law, which describes the solubility of the gas in a liquid under specific pressure conditions. Since its introduction by Hashim et al. in 2019, HGSO has gained significant attention for its unique features, including minimal adaptive parameters and a balanced exploration-exploitation trade-off, leading to favorable convergence. This study provides an up-to-date survey of HGSO, covering the walk through the historical development of HGSO, its modifications, and hybridizations with other algorithms, showcasing its adaptability and potential for synergy. Recent variants of HGSO are categorized into modified, hybridized, and multi-objective versions, and the review explores its main applications, demonstrating its effectiveness in solving complex problems. The evaluation includes a discussion of the algorithm's strengths and weaknesses. This comprehensive review, featuring graphical and tabular comparisons, not only indicates potential future directions in the field but also serves as a valuable resource for researchers seeking a deep understanding of HGSO and its advanced versions. As physics-based metaheuristic algorithms gain prominence for solving intricate optimization problems, this study provides insights into the adaptability and applications of HGSO across diverse domains.

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Section: B Hybridized Versions Of Hgsomentioning

confidence: 99%

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

IEEE Access

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“…In 2022, Patra et al 15 proposed a Deep Maxout Network with Dolphin-based Henry Gas Solubility Optimization. Firstly, they used a Gaussian filter.…”

Section: Related Workmentioning

confidence: 99%

LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification

Ren

Zhang

Wang

2022

Technol Cancer Res Treat

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Objective: The only possible solution to increase the patients’ fatality rate is lung cancer early-stage detection. Recently, deep learning techniques became the most promising methods in medical image analysis compared with other numerous computer-aided diagnostic techniques. However, deep learning models always get lower performance when the model is overfitting. Methods: We present a Lung Cancer Data Augmented Ensemble (LCDAE) framework to solve the overfitting and lower performance problems in the lung cancer classification tasks. The LCDAE has 3 parts: The Lung Cancer Deep Convolutional GAN, which can synthesize images of lung cancer; A Data Augmented Ensemble model (DA-ENM), which ensembled 6 fine-tuned transfer learning models for training, testing, and validating on a lung cancer dataset; The third part is a Hybrid Data Augmentation (HDA) which combines all the data augmentation techniques in the LCDAE. Results: By comparing with existing state-of-the-art methods, the LCDAE obtains the best accuracy of 99.99%, the precision of 99.99%, and the F1-score of 99.99%. Conclusion: Our proposed LCDAE can overcome the overfitting issue for the lung cancer classification tasks by applying different data augmentation techniques, our method also has the best performance compared to state-of-the-art approaches.

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Deep volcanic residual U-Net for nodal metastasis (Nmet) identification from lung cancer

Ramkumar,

Kalirajan,

Kumar

et al. 2023

Biomed. Eng. Lett.

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Fractional rider gradient descent applied U-Net based segmentation with optimal deep maxout network for lung cancer classification using histopathological images

Cited by 3 publications

References 22 publications

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

LCDAE: Data Augmented Ensemble Framework for Lung Cancer Classification

Deep volcanic residual U-Net for nodal metastasis (Nmet) identification from lung cancer

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