Utilizing convolutional neural networks to classify monkeypox skin lesions

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Sept. 3, 2023

Monkeypox is a rare viral disease that can cause severe illness in humans, presenting with skin lesions and rashes. However, accurately diagnosing monkeypox based on visual inspection of the be challenging time-consuming, especially resource-limited settings where laboratory tests may not available. In recent years, deep learning methods, particularly Convolutional Neural Networks (CNNs), have shown great potential image recognition classification tasks. To this end, study proposes an approach using CNNs to classify lesions. Additionally, optimized CNN model Grey Wolf Optimizer (GWO) algorithm, resulting significant improvement accuracy, precision, recall, F1-score, AUC compared non-optimized model. The GWO optimization strategy enhance performance models similar achieved impressive accuracy 95.3%, indicating optimizer has improved model's ability discriminate between positive negative classes. proposed several benefits for improving efficiency diagnosis surveillance. It could enable faster more accurate lesions, leading earlier detection better patient outcomes. Furthermore, crucial public health implications controlling preventing outbreaks. Overall, offers novel highly effective monkeypox, which real-world applications.

Language: Английский

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Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review

Diagnostics, Journal Year: 2023, Volume and Issue: 13(5), P. 824 - 824

Published: Feb. 21, 2023

Monkeypox or Mpox is an infectious virus predominantly found in Africa. It has spread to many countries since its latest outbreak. Symptoms such as headaches, chills, and fever are observed humans. Lumps rashes also appear on the skin (similar smallpox, measles, chickenpox). Many artificial intelligence (AI) models have been developed for accurate early diagnosis. In this work, we systematically reviewed recent studies that used AI mpox-related research. After a literature search, 34 fulfilling prespecified criteria were selected with following subject categories: diagnostic testing of mpox, epidemiological modeling mpox infection spread, drug vaccine discovery, media risk management. beginning, detection using various modalities was described. Other applications ML DL mitigating categorized later. The machine deep learning algorithms their performance discussed. We believe state-of-the-art review will be valuable resource researchers data scientists developing measures counter spread.

Language: Английский

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Deep hybrid model for Mpox disease diagnosis from skin lesion images

International Journal of Imaging Systems and Technology, Journal Year: 2024, Volume and Issue: 34(2)

Published: Feb. 26, 2024

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

Language: Английский

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Squid Game Optimizer (SGO): a novel metaheuristic algorithm

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: April 1, 2023

In this paper, Squid Game Optimizer (SGO) is proposed as a novel metaheuristic algorithm inspired by the primary rules of traditional Korean game. game multiplayer with two objectives: attackers aim to complete their goal while teams try eliminate each other, and it usually played on large, open fields no set guidelines for size dimensions. The playfield often shaped like squid and, according historical context, appears be around half standard basketball court. mathematical model developed based population solution candidates random initialization process in first stage. are divided into groups offensive defensive players player goes among start fight which modeled through movement toward players. By considering winning states both sides calculated objective function, position updating conducted new vectors produced. To evaluate effectiveness SGO algorithm, 25 unconstrained test functions 100 dimensions used, alongside six other commonly used metaheuristics comparison. independent optimization runs algorithms pre-determined stopping condition ensure statistical significance results. Statistical metrics such mean, deviation, mean required function evaluations calculated. provide more comprehensive analysis, four prominent tests including Kolmogorov-Smirnov, Mann-Whitney, Kruskal-Wallis used. Meanwhile, ability suggested SGOA assessed cutting-edge real-world problems newest CEC 2020, demonstrate outstanding performance dealing these complex problems. overall assessment indicates that can competitive remarkable outcomes benchmark

Language: Английский

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Detection of Monkeypox Disease from Human Skin Images with a Hybrid Deep Learning Model

Diagnostics, Journal Year: 2023, Volume and Issue: 13(10), P. 1772 - 1772

Published: May 17, 2023

Monkeypox, a virus transmitted from animals to humans, is DNA with two distinct genetic lineages in central and eastern Africa. In addition zootonic transmission through direct contact the body fluids blood of infected animals, monkeypox can also be person skin lesions respiratory secretions an person. Various occur on individuals. This study has developed hybrid artificial intelligence system detect images. An open source image dataset was used for multi-class structure consisting chickenpox, measles, normal classes. The data distribution classes original unbalanced. augmentation preprocessing operations were applied overcome this imbalance. After these operations, CSPDarkNet, InceptionV4, MnasNet, MobileNetV3, RepVGG, SE-ResNet Xception, which are state-of-the-art deep learning models, detection. order improve classification results obtained unique model specific created by using highest-performing models long short-term memory (LSTM) together. proposed detection, test accuracy 87% Cohen's kappa score 0.8222.

Language: Английский

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Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods

Diagnostics, Journal Year: 2023, Volume and Issue: 13(14), P. 2391 - 2391

Published: July 17, 2023

The monkeypox virus poses a novel public health risk that might quickly escalate into worldwide epidemic. Machine learning (ML) has recently shown much promise in diagnosing diseases like cancer, finding tumor cells, and COVID-19 patients. In this study, we have created dataset based on the data both collected published by Global Health used World Organization (WHO). Being entirely textual, shows relationship between symptoms disease. been analyzed, using gradient boosting methods such as Extreme Gradient Boosting (XGBoost), CatBoost, LightGBM along with other standard machine Support Vector (SVM) Random Forest. All these compared. research aims to provide an ML model for diagnosis of monkeypox. Previous studies only examined disease images. best performance belonged XGBoost, accuracy 1.0 reviews. To check model's flexibility, k-fold cross-validation is used, reaching average 0.9 5 different splits test set. addition, Shapley Additive Explanations (SHAP) helps examining explaining output XGBoost model.

Language: Английский

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AI-Based Approaches for the Diagnosis of Mpox: Challenges and Future Prospects

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(6), P. 3585 - 3617

Published: March 26, 2024

Language: Английский

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

Biomimetics, Journal Year: 2023, Volume and Issue: 8(3), P. 313 - 313

Published: July 16, 2023

The virus that causes monkeypox has been observed in Africa for several years, and it linked to the development of skin lesions. Public panic anxiety have resulted from deadly repercussions infections following COVID-19 pandemic. Rapid detection approaches are crucial since reached a pandemic level. This study's overarching goal is use metaheuristic optimization boost performance feature selection classification methods identify lesions as indicators event Deep learning transfer used extract necessary features. GoogLeNet network deep framework extraction. In addition, binary implementation dipper throated (DTO) algorithm selection. decision tree classifier then label selected set optimized using continuous version DTO improve accuracy. Various evaluation compare contrast proposed approach other competing metrics: accuracy, sensitivity, specificity,

Language: Английский

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

Biomimetics, Journal Year: 2023, Volume and Issue: 8(3), P. 270 - 270

Published: June 26, 2023

Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated type can be reduced early detection. Nonetheless, a skilled professional always necessary manually diagnose malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, inadequate training models. In paper, we developed novel computationally automated biological mechanism for categorizing breast cancer. Using optimization approach Advanced Al-Biruni Earth Radius (ABER) algorithm, boosting classification realized. stages framework include data augmentation, feature extraction using AlexNet transfer learning, optimized convolutional neural network (CNN). learning CNN improved accuracy when results are compared recent approaches. Two publicly available datasets utilized evaluate framework, average 97.95%. To ensure statistical significance difference between methodology, additional tests conducted, analysis variance (ANOVA) Wilcoxon, addition evaluating various metrics. these emphasized effectiveness methodology current methods.

Language: Английский

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Optimizing HCV Disease Prediction in Egypt: The hyOPTGB Framework

Diagnostics, Journal Year: 2023, Volume and Issue: 13(22), P. 3439 - 3439

Published: Nov. 13, 2023

The paper focuses on the hepatitis C virus (HCV) infection in Egypt, which has one of highest rates HCV world. high prevalence is linked to several factors, including use injection drugs, poor sterilization practices medical facilities, and low public awareness. This introduces a hyOPTGB model, employs an optimized gradient boosting (GB) classifier predict disease Egypt. model's accuracy enhanced by optimizing hyperparameters with OPTUNA framework. Min-Max normalization used as preprocessing step for scaling dataset values using forward selection (FS) wrapped method identify essential features. study contains 1385 instances 29 features available at UCI machine learning repository. authors compare performance five models, decision tree (DT), support vector (SVM), dummy (DC), ridge (RC), bagging (BC), model. system's efficacy assessed various metrics, accuracy, recall, precision, F1-score. model outperformed other achieving 95.3% rate. also compared against models proposed who same dataset.

Language: Английский

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