Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm

Alhussan, Amel Ali; Towfek, S. K.; Abdelhamid, Abdelaziz A.; Ibrahim, Abdelhameed; Eid, Marwa M.; Khafaga, Doaa Sami; Khodadadi, Nima; Abualigah, Laith; Saber, Mohamed

doi:10.3390/biomimetics8030313

Cited by 14 publications

(3 citation statements)

References 59 publications

(51 reference statements)

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“…In the domain of DL-based classification of MPox disease, several studies have focused on classifying MPox skin lesions using DL methods, such as VGG-16, 26 ResNet50, 27 and InceptionV3. 28 Another study utilized the Xception 29 TL model in combination with Grad-Cam and LIME techniques, achieving high accuracy and F1-score.…”

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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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%

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

“…To streamline the analysis, one-way ANOVA, a well-established and reliable methodology for feature reduction, will be employed. This technique will be used to select a subset of the most significant features, allowing a focus on the most relevant aspects of the data [36][37][38].…”

Section: One-way Analysis Of Variance (Anova) Feature Selectionmentioning

confidence: 99%

A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilising MFPT Bearing Dataset

Hejazi,

Packianather,

Liu

2024

Machines

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This research presents a novel Customised Load Adaptive Framework (CLAF) for fault classification in Induction Motors (IMs), utilising the Machinery Fault Prevention Technology (MFPT) bearing dataset. CLAF represents a pioneering approach that extends traditional fault classification methodologies by accounting for load variations and dataset customisation. Through a meticulous two-phase process, it unveils load-dependent fault subclasses that have not been readily identified in traditional approaches. Additionally, new classes are created to accommodate the dataset’s unique characteristics. Phase 1 involves exploring load-dependent patterns in time and frequency domain features using one-way Analysis of Variance (ANOVA) ranking and validation via bagged tree classifiers. In Phase 2, CLAF is applied to identify mild, moderate, and severe load-dependent fault subclasses through optimal Continuous Wavelet Transform (CWT) selection through Wavelet Singular Entropy (WSE) and CWT energy analysis. The results are compelling, with a 96.3% classification accuracy achieved when employing a Wide Neural Network to classify proposed load-dependent fault subclasses. This underscores the practical value of CLAF in enhancing fault diagnosis in IMs and its future potential in advancing IM condition monitoring.

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“…Hence, this paper will discuss the features changing when the fault occurs and relate to the timefrequency domain. Features will be reduced using one-way ANOVA as it is considered a well-known and solid methodology for feature reduction employed for subset feature selection to identify the most significant features in [37][38][39].…”

Section: One-way Analysis Of Variance (Anova) Features Selectionmentioning

confidence: 99%

A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilizing MFPT Bearing Dataset

Hejazi,

Packianather,

Liu

2023

Preprint

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This research presents a novel Customised Load Adaptive Framework (CLAF) for fault classifica-tion in Induction Motors (IMs), utilizing the Machinery Fault Prevention Technology (MFPT) Bearing Dataset. CLAF represents a pioneering approach that extends traditional fault classification methodologies by accounting for load variations and dataset customization. Through a meticulous two-phase process, it unveils load-dependent fault subclasses that have not been readily identified in traditional approaches. Additionally, new classes are created to accommodate the dataset's unique characteristics. Phase 1 involves the exploration of load-dependent patterns in time and frequency domain features using one-way Analysis of Variance (ANOVA) ranking and validation via bagged tree classifiers. In Phase 2, CLAF is applied to identify mild, moderate, and severe load-dependent fault subclasses through optimal Continuous Wavelet Transform (CWT) selection through Wavelet Singular Entropy (WSE) and CWT energy analysis. The results are compelling, with a 96.3% classification accuracy achieved when employing a wide neural network to classify proposed load-dependent fault subclasses. This underscores the practical value of CLAF in en-hancing fault diagnosis in IMs and its future potential in advancing IM condition monitoring.

show abstract

Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm

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

A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilising MFPT Bearing Dataset

A Novel Customised Load Adaptive Framework for Induction Motor Fault Classification Utilizing MFPT Bearing Dataset

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