Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization

Alhussan, Amel Ali; Abdelhamid, Abdelaziz A.; Towfek, S. K.; Ibrahim, Abdelhameed; Abualigah, Laith; Khodadadi, Nima; Khafaga, Doaa Sami; Al-Otaibi, Shaha; Ahmed, Ali

doi:10.3390/biomimetics8030270

Cited by 14 publications

(3 citation statements)

References 59 publications

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“…Furthermore, novel approaches integrating AI‐based optimization techniques have been proposed for MPox diagnosis. These approaches involve fine‐tuning customized CNN layers using AI‐Biruni Earth Radius (BER) 3 optimization‐based stochastic fractal search (BERSFS) 22 and employing optimization algorithms like sine cosine (SC) and particle swarm (PS) for feature selection with multilayer perceptron (MLP) classifiers. Additionally, several studies utilized various CNN architectures 13 to differentiate between MPox and non‐MPox cases using both MSID and MSLD datasets.…”

Section: Related Workmentioning

confidence: 99%

“…The detection and diagnosis of human MPox [19][20][21] have received increasing attention in the scientific literature, particularly with the recent global spread of MPox infection. However, research is insufficient in applying CAD 22 for primary diagnosis due to the limited availability of publicly accessible datasets for training and testing purposes. CNNs 23 have demonstrated remarkable ability in analyzing medical images and identifying patterns related to various diseases, making them promising candidates for MPox diagnosis.…”

Section: Related Workmentioning

confidence: 99%

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Deep hybrid model for Mpox disease diagnosis from skin lesion images

Khan,

Asif,

Bilal

et al. 2024

Int J Imaging Syst Tech

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This research presents DNLR‐NET, a novel model designed for automated and accurate diagnosis of MPox disease. The model's performance is constructed and validated using a carefully collected MPox dataset from online repositories. DNLR‐NET begins by extracting deep features from the DenseNet201 pre‐trained model, which exhibited superior performance compared to other models during the comparison. The deep features obtained from each dense layer are then used to train six classifiers, among which logistic regression showcases the best performance with the extracted deep, dense feature. A comparative study with earlier advanced CNN models classifying the same dataset demonstrates that DNLR‐NET achieves an impressive accuracy of 97.55%, outperforming the base DenseNet201 model, which only attains 95.91% accuracy. This accuracy emphasizes the efficacy of combining deep features with logistic regression. A Grid Search algorithm is employed for optimal hyperparameter extraction, creating multiple unified deep feature sets and achieving the highest classification accuracy. The fusion of deep features with logistic regression yields superior results compared to ensemble techniques such as random forest and support vector machines and also reduces training time complexity. DNLR‐NET surpasses existing models, ML classifiers, and pre‐trained models, demonstrating its effectiveness and potential for clinical implementation in diagnosing MPox. The promising outcomes of advantage deep learning algorithms, particularly DenseNet201 transfer learning, highlight the significance of adopting transfer learning methodologies for CNN‐based MPox diagnosis in clinical settings. Researchers and clinicians are strongly encouraged to explore and implement these techniques to improve the accuracy and efficiency of MPox diagnosis.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep hybrid model for Mpox disease diagnosis from skin lesion images

Khan,

Asif,

Bilal

et al. 2024

Int J Imaging Syst Tech

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show abstract

“…This approach has achieved significant success in a variety of tasks for which training data is insufficient or of poor quality [1,2]. Model fine-tuning, as a commonly used transfer learning technique, effectively improves the learning performance by transferring the parameters of the pre-trained neural network (NN) model from the source domain to the target task and fine-tuning them [3,4]. Such fine-tuning techniques are already successfully used in various fields such as computer vision [5], natural language processing [6], speech recognition [7], recommendation systems [8], and medical diagnosis [9].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-source Domain Collaborative Fine-tuning for Transfer Learning

Feng,

Yang,

Tan

et al. 2023

Preprint

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Fine-tuning is an important technique in transfer learning that has achieved significant success in tasks that lack training data. However, as it is difficult to extract effective features for single-source domain fine-tuning when the data distribution difference between the source and the target domain is large, we propose a transfer learning framework based on multi-source domain called adaptive multi-source domain collaborative fine-tuning to address this issue. This approach utilizes multiple source domain models for collaborative fine-tuning, thereby improving the model's feature extraction capability in the target task. Specifically, this approach employs an adaptive multi-source domain layer selection strategy to customize appropriate layer fine-tuning schemes for the target task among multiple source domain models. The objective is to extract more efficient features. Furthermore, a novel multi-source domain collaborative loss function is designed to facilitate the precise extraction of target data features by each source domain model. Simultaneously, it works towards minimizing the output difference among various source domain models, thereby enhancing the adaptability of the source domain model to the target data. In order to validate the effectiveness of the proposed the adaptive multi-source domain collaborative fine-tuning framework, it is analyzed and compared with the most widely used fine-tuning methods by applying them to seven publicly available visual categorization datasets commonly employed in transfer learning. Experimental results demonstrate that, in comparison with the existing fine-tuning approaches, our method not only enhances the accuracy of feature extraction in the model but also provides precise layer fine-tuning schemes for the target task, resulting in state-of-the-art performance.

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Privacy-Preserving Breast Cancer Classification: A Federated Transfer Learning Approach

Dharani Devi,

et al. 2024

J Digit Imaging. Inform. med.

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Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization

Cited by 14 publications

References 59 publications

Deep hybrid model for Mpox disease diagnosis from skin lesion images

Deep hybrid model for Mpox disease diagnosis from skin lesion images

Adaptive Multi-source Domain Collaborative Fine-tuning for Transfer Learning

Privacy-Preserving Breast Cancer Classification: A Federated Transfer Learning Approach

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