Monkeypox Disease Recognition Model Using SE-InceptionV3 with Improved Accuracy

Konsep Inti

Enhancing monkeypox disease detection using an improved SE-InceptionV3 model with SENet module and L2 regularization.

Abstrak

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.

Statistik

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.

Kutipan

"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."

Wawasan Utama Disaring Dari

Monkeypox disease recognition model based on improved SE-InceptionV3

by Junzhuo Chen... pada arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10087.pdf

Pertanyaan yang Lebih Dalam

How can this advanced recognition model be implemented in real-world healthcare settings

The implementation of this advanced recognition model in real-world healthcare settings can revolutionize disease diagnosis and treatment. One key application would be integrating the model into telemedicine platforms, allowing healthcare providers to remotely diagnose skin conditions like monkeypox with high accuracy. This could improve access to specialized care in underserved areas where dermatologists are scarce. Additionally, the model could be used in conjunction with medical imaging devices to assist clinicians in making faster and more accurate diagnoses. By automating the process of identifying skin lesions, the model can streamline workflows, reduce diagnostic errors, and ultimately improve patient outcomes.

What are potential limitations or biases that could affect the model's performance in diverse populations

While the advanced recognition model shows promising results in detecting monkeypox lesions accurately, there are potential limitations and biases that could affect its performance in diverse populations. One limitation is dataset bias - if the training data predominantly consists of images from a specific demographic group or geographic region, the model may not generalize well to populations with different characteristics. Moreover, variations in skin tones and textures among different ethnicities could impact the model's ability to accurately identify skin conditions across diverse populations. To address these limitations, it is crucial to ensure dataset diversity by including images from various demographics and regions during training.

How might advancements in deep learning impact future research on disease diagnosis beyond monkeypox

Advancements in deep learning have significant implications for future research on disease diagnosis beyond monkeypox. These advancements open up possibilities for developing models that can detect a wide range of medical conditions based on image analysis. For instance, similar models could be trained to recognize other dermatological diseases such as melanoma or psoriasis with high accuracy. Furthermore, deep learning techniques can be applied to various medical imaging modalities beyond just visible light images - including MRI scans, X-rays, and CT scans - enabling automated diagnosis across multiple specialties within healthcare. The continuous evolution of deep learning algorithms will likely lead to more sophisticated models capable of early detection and personalized treatment recommendations for complex diseases across diverse patient populations.