MPOX SKIN LESION DETECTION MODEL BY IMPROVED DEEP LEARNING MODEL

Year 2026, Volume: 14 Issue: 1, 451 - 472, 01.03.2026

Fatma Akalın , Yasin Özkan

https://doi.org/10.36306/konjes.1627267

https://izlik.org/JA59TP39KY

Abstract

Artificial intelligence is a framework that enables human cognitive abilities and intelligent behaviors to be performed by machines. It has strong potential to make successful inferences and offer quick solutions for diseases in the field of health. Integrating this potential into the detection and diagnosis process of monkeypox, which is characterized as a pandemic case, is valuable in terms of controllability of the epidemic. Because monkeypox is a disease with a high transmission rate. In addition, the clinical features of the disease are easily confused with skin diseases such as measles, chickenpox and eczema. On the other hand, since symptoms usually appear about 3 weeks after the virus enters the body, contact with respiratory droplets, bodily fluids or infected objects, which play an important role in the spread of the disease during this period, will increase the rate of spread. The declaration through the Centers for Disease Control and Prevention that 99,518 cases have been confirmed as of August 2024 confirms this situation.
In this regard, artificial intelligence solutions stand out for providing fast and accurate diagnosis. For this, various deep learning models (VGG16, MobileNetV2, ResNet50, DenseNet169, InceptionV3, XceptionV3, EfficientB7, AlexNet) were used to classify the Mpox Skin Lesion Dataset Version 2.0 dataset in the first stage. In the second stage, six different regularization methods were proposed to optimize performance. In the third stage, the success rate was optimized by integrating the improved hybrid loss function. In the fourth stage, the improved approach was applied to the augmented dataset. The results show that the proposed approach improves the accuracy rate by approximately 6% compared to the original classification architecture.

Keywords

Classification , Data augmentation , Loss function , Monkeypox virus , Regularization function , Transfer learning.

Thanks

We would like to thank CHATGBT for their support in finding solutions to errors encountered in the code, providing a different perspective, and translating it into English.

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