DeepGenMon: A Novel Framework for Monkeypox Classification Integrating Lightweight Attention-Based Deep Learning and a Genetic Algorithm

Diagnostics, Journal Year: 2025, Volume and Issue: 15(2), P. 130 - 130

Published: Jan. 8, 2025

Background: The rapid global spread of the monkeypox virus has led to serious issues for public health professionals. According related studies, and other types skin conditions can through direct contact with infected animals, humans, or contaminated items. This disease cause fever, headaches, muscle aches, enlarged lymph nodes, followed by a rash that develops into lesions. To facilitate early detection monkeypox, researchers have proposed several AI-based techniques accurately classifying identifying condition. However, there is still room improvement detect classify cases. Furthermore, currently pre-trained deep learning models consume extensive resources achieve accurate classification monkeypox. Hence, these often need significant computational power memory. Methods: paper proposes novel lightweight framework called DeepGenMonto various diseases, such as chickenpox, melasma, others. suggested leverages an attention-based convolutional neural network (CNN) genetic algorithm (GA) enhance accuracy while optimizing hyperparameters model. It first applies attention mechanism highlight assign weights specific regions image are relevant model's decision-making process. Next, CNN employed process visual input extract hierarchical features data multiple classes. Finally, CNN's adjusted using robustness accuracy. Compared state-of-the-art (SOTA) models, DeepGenMon design requires significantly lower easier train few parameters. Its effective integration GA further enhances its performance, making it particularly well suited low-resource environments. evaluated on two datasets. dataset comprises 847 images diverse second contains 659 classified categories. Results: model demonstrates superior performance compared SOTA across key evaluation metrics. On 1, achieves precision 0.985, recall 0.984, F-score 0.985. Similarly, 2, attains 0.981, 0.982, 0.982. Moreover, findings demonstrate ability inference time 2.9764 s 1 2.1753 2. Conclusions: These results also show DeepGenMon's effectiveness in different conditions, highlighting potential reliable tool clinical settings.

Language: Английский

Ultra-Sensitive Dengue NS1 Protein Detection via Asymmetric Source-Drain, Heterojunction and Dual-Dielectric Cavities in a Uniform Tunnel FET Biosensor

Results in Engineering, Journal Year: 2024, Volume and Issue: 24, P. 103318 - 103318

Published: Nov. 4, 2024

Language: Английский