핵심 개념
Enhancing monkeypox disease detection using an improved SE-InceptionV3 model with SENet module and L2 regularization.
초록
In response to the global spread of monkeypox, a study introduces an enhanced SE-InceptionV3 model for accurate disease recognition. The model incorporates the SENet module and L2 regularization within the InceptionV3 framework to improve monkeypox lesion detection. Utilizing the Kaggle monkeypox dataset, the model achieves a notable accuracy of 96.71% on the test set, surpassing traditional methods and other deep learning models. The study highlights the effectiveness of the model in differentiating monkeypox lesions in diverse cases through precision, recall, and F1 score improvements. By embedding the SENet module, feature representation is enhanced, while L2 regularization ensures robust generalization against overfitting. The research not only showcases advanced CNN architectures in medical diagnostics but also paves the way for further optimization and research in disease recognition models.
통계
The model achieves a noteworthy accuracy of 96.71% on the test set.
The ResNet50 model achieved an accuracy of 82.96% on the Monkeypox Skin Damage Dataset.
Malathi Velu's team achieved 95% accuracy using a Q-learning-based method on a Kaggle monkeypox dataset.
Amir Sorayaie Azar et al. achieved 95.18% accuracy using an improved DenseNet-201 model.
Entesar Hamed I. Eliwa's team combined a CNN model with GWO optimizer to achieve 95.3% accuracy.
DenseNet121-TL achieved an accuracy of 83.59 ± 2.11% on the HAM10000 dataset.
인용구
"Our model demonstrates a noteworthy accuracy of 96.71% on the test set."
"The SENet module significantly elevates feature representation."
"L2 regularization ensures robust generalization in dealing with monkeypox lesions."