A Streamlined Approach towards Monkeypox Detection DOI Creative Commons

Sarvesh Kulkarni,

Jay Oza, Abhijit Patil

et al.

Published: Dec. 4, 2023

<p>Monkeypox has recently emerged as a public health emergency with rising cases worldwide. Early clinical diagnosis is challenging due to symptom overlap other diseases, but characteristic skin lesions provide distinguishing visual cues. This work proposes deep convolutional neural network (CNN) tailored for automated monkeypox screening from lesion images. A dataset of over 3000 dermatological images was compiled, data augmentation enhance diversity. The CNN architecture comprised blocks feature extraction and dense layers classification. Rigorous training cross-validation were conducted 100 epochs optimize model performance. On an unseen test set, the achieved 86.87\% accuracy in classifying lesions, 94\% precision, 79\% recall 86\% F1-score. These metrics better than baseline models, indicating reliable potential. Though overlooked some atypical presentations, successes showcase utility mass case-finding. As monitoring intensifies, robust computer vision approaches can assist clinicians through explainable, real-time forecasts. Prospective validation across demographics integration workflows warranted before full-scale deployment. Overall, study demonstrates learning's promise tackling outbreak enhanced diagnosis.</p>

Language: Английский

CGO-ensemble: Chaos game optimization algorithm-based fusion of deep neural networks for accurate Mpox detection DOI

Sohaib Asif,

Ming Zhao, Yangfan Li

et al.

Neural Networks, Journal Year: 2024, Volume and Issue: 173, P. 106183 - 106183

Published: Feb. 16, 2024

Language: Английский

Citations

15
AI-Based Approaches for the Diagnosis of Mpox: Challenges and Future Prospects DOI

Sohaib Asif,

Ming Zhao, Yangfan Li

et al.

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

Published: March 26, 2024

Language: Английский

Citations

11
Transmission dynamics, complications and mitigation strategies of the current mpox outbreak: A comprehensive review with bibliometric study DOI
Ranjan K. Mohapatra, Puneet Kumar Singh, Francesco Branda

et al.

Reviews in Medical Virology, Journal Year: 2024, Volume and Issue: 34(3)

Published: May 1, 2024

Abstract As the mankind counters ongoing COVID‐19 pandemic by novel severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2), it simultaneously witnesses emergence of mpox virus (MPXV) that signals at global spread and could potentially lead to another pandemic. Although MPXV has existed for more than 50 years now with most human cases being reported from endemic West Central African regions, disease is recently in non‐endemic regions too affect countries. Controlling important due its potential danger a spread, causing morbidity mortality. The article highlights transmission dynamics, zoonosis potential, complication mitigation strategies infection, concludes suggested ‘one health’ approach better management, control prevention. Bibliometric analyses data extend understanding provide leads on research trends, need revamp critical healthcare interventions. Globally published mpox‐related literature does not align well areas/regions occurrence which should ideally have been scenario. Such demographic geographic gaps between location work epicentres be bridged greater effective translation outputs pubic systems, suggested.

Language: Английский

Citations

11
Interdisciplinary Approach to Monkeypox Prevention: Integrating Nanobiosensors, Nanovaccines, Artificial Intelligence, Visual Arts, and Social Sciences DOI Creative Commons
Vishal Chaudhary,

Lucky Lucky,

Harsh Sable

et al.

Small Structures, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

To effectively address crisis emergence of new virus such as monkeypox, a collective and collaborative effort between scientists, engineers, innovators, artists from all ages, regions, diverse fields is required. This review explores holistic approach to addressing the monkeypox by integrating nanobiosensors, artificial intelligence, visual arts, humanities, social sciences. Traditional diagnostic methods are often limited time, accessibility, accuracy, but advancement point‐of‐care smart nanobiosensors offers promising shift toward rapid, precise, accessible diagnostics. They enhance ability screen, diagnose, monitor infections efficiently, contributing better disease management. Beyond technological innovation, essential role sciences in fostering public engagement, understanding, acceptance tools emphasized. Visual arts can illustrate scientific concepts, making them more relatable, while storytelling through various media reduce stigma promote preventive measures. Social provide insights into cultural attitudes, behaviors, health challenges, ensuring that solutions integrated communities. By combining these disciplines, this presents comprehensive framework for resilient global system aligns with One Health principles, emphasizing interconnectedness human, animal, environmental health.

Language: Английский

Citations

1
Classification of monkeypox images using Al-Biruni earth radius optimization with deep convolutional neural network DOI Creative Commons
Amal H. Alharbi

AIP Advances, Journal Year: 2024, Volume and Issue: 14(6)

Published: June 1, 2024

There is a connection that has been established between the virus responsible for monkeypox and formation of skin lesions. This detected in Africa many years. Our research centered around detection lesions as potential indicators during pandemic. primary objective to utilize metaheuristic optimization techniques improve performance feature selection classification algorithms. In order accomplish this goal, we make use deep learning transfer technique extract attributes. The GoogleNet network, framework, used carry out extraction. Furthermore, process conducted using binary version dynamic Al-Biruni earth radius (DBER). After that, convolutional neural network assign labels selected features from collection. To accuracy, adjustments are made by utilizing continuous DBER algorithm. We range metrics analyze different assessment methods, including sensitivity, specificity, positive predictive value (P-value), negative (N-value), F1-score. They were compared each other. All metrics, F1-score, P-value, N-value, achieved high values 0.992, 0.991, 0.993, respectively. outcomes combining with network. optimizing parameters proposed method an impressive overall accuracy rate 0.992.

Language: Английский

Citations

4
Leveraging Convolutional Neural Networks for Disease Detection in Vegetables: A Comprehensive Review DOI Creative Commons
Muhammad Mahmood ur Rehman,

Jizhan Liu,

Aneela Nijabat

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(10), P. 2231 - 2231

Published: Sept. 27, 2024

Timely and accurate detection of diseases in vegetables is crucial for effective management mitigation strategies before they take a harmful turn. In recent years, convolutional neural networks (CNNs) have emerged as powerful tools automated disease crops due to their ability learn intricate patterns from large-scale image datasets make predictions samples that are given. The use CNN algorithms important vegetable like potatoes, tomatoes, peppers, cucumbers, bitter gourd, carrot, cabbage, cauliflower critically examined this review paper. This examines the most state-of-the-art techniques, datasets, difficulties related these crops’ CNN-based systems. Firstly, we present summary architecture its applicability classify tasks based on images. Subsequently, explore applications identification crops, emphasizing relevant research, performance measures. Also, benefits drawbacks methods, covering problems with computational complexity, model generalization, dataset size, discussed. concludes by highlighting revolutionary potential transforming crop diagnosis strategies. Finally, study provides insights into current limitations regarding usage computer field detection.

Language: Английский

Citations

4
Robustly detecting mpox and non-mpox using a deep learning framework based on image inpainting DOI Creative Commons

Yujun Cao,

Yubiao Yue, Xiaoming Ma

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 10, 2025

Language: Английский

Citations

0
Mpox-XDE: an ensemble model utilizing deep CNN and explainable AI for monkeypox detection and classification DOI Creative Commons
Dip Kumar Saha,

Sana Rafi,

M. F. Mridha

et al.

BMC Infectious Diseases, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 25, 2025

The daily surge in cases many nations has made the growing number of human monkeypox (Mpox) an important global concern. Therefore, it is imperative to identify Mpox early prevent its spread. majority studies on identification have utilized deep learning (DL) models. However, research developing a reliable method for accurately detecting stages still lacking. This study proposes ensemble model composed three improved DL models more classify phases. We used widely recognized Skin Images Dataset (MSID), which includes 770 images. enhanced Swin Transformer (SwinViT), proposed Mpox-XDE, and modified models-Xception, DenseNet201, EfficientNetB7-were used. To generate model, were combined via Softmax layer, dense flattened 65% dropout. Four neurons final layer dataset into four categories: chickenpox, measles, normal, Mpox. Lastly, average pooling implemented actual class. Mpox-XDE performed exceptionally well, achieving testing accuracy, precision, recall, F1-score 98.70%, 98.90%, 98.80%, respectively. Finally, popular explainable artificial intelligence (XAI) technique, Gradient-weighted Class Activation Mapping (Grad-CAM), was applied convolutional overlaid areas that effectively highlight each illness class dataset. methodology will aid professionals diagnosing patient's condition.

Language: Английский

Citations

0
Opportunities and challenges in implementing CRISPR-based point-of-care testing for Monkeypox detection DOI Creative Commons
Md. Ahasan Ahamed, Weihua Guan

BioTechniques, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 5

Published: April 3, 2025

Language: Английский

Citations

0
Development of a Robust CNN Model for Mango Leaf Disease Detection and Classification: A Precision Agriculture Approach DOI
Amit Kumar Pathak,

Ponkaj Saikia,

Sanghamitra Dutta

et al.

ACS Agricultural Science & Technology, Journal Year: 2024, Volume and Issue: 4(8), P. 806 - 817

Published: July 16, 2024

In recent years, convolutional neural network (CNN) models and deep learning techniques have gained significant attention for plant disease detection. Despite advances, achieving high accuracy across diverse classes remains challenging. Existing CNN demonstrated moderate in classifying a limited number of mango leaf diseases. So, crucial necessity exists to broaden the scope precision. Our investigation introduces model that achieves an impressive 99% eight Using advanced data processing, image augmentation, feature extraction methodologies rooted artificial intelligence learning, we systematically explored over 20 architectures various hyperparameters develop robust model. Given global significance cultivation, our was rigorously trained tested reliability. Detailed results materials are available on GitHub. Additionally, integrated into Android app, "Mango-SCN", designed easy use managing diseases, accessible even nonexperts.

Language: Английский

Citations

2