Detection of Monkeypox disease from skin lesion images using deep learning methods

Authors : Muhammet Talha Engin, Kemal Adem

Pages : 1240-1252

Doi:10.28948/ngumuh.1436907

View : 155 | Download : 122

Publication Date : 2024-10-15

Article Type : Research Paper

Abstract :Monkeypox is a disease that, while less deadly and contagious than COVID-19, could pose a global pandemic threat. In the field of medical imaging, deep learning techniques offer promising results in the diagnosis of diseases. This study develops deep learning models using skin lesion images for early diagnosis of monkeypox. The research is divided into two key sections. In the first section, a deep learning model is developed using the Monkeypox Skin Image Dataset (MSID). The second section focuses on a model trained on a combined dataset, which merges the Monkeypox Skin Image, Monkeypox Master, and Monkeypox Original Image Datasets, referred to as HYBRID. The MSID dataset comprises 806 Monkeypox and 690 Non-Monkeypox images for training, along with 309 Monkeypox and 292 Non-Monkeypox images for testing, resulting in a total of 2,097 images of skin lesions with and without monkeypox. The HYBRID dataset includes 1,088 Monkeypox and 1,896 Non-Monkeypox images for training, as well as 468 Monkeypox and 812 Non-Monkeypox images for testing, resulting in a total of 4,264 skin lesion images. Five distinct deep learning models—DenseNet201, InceptionResNetV2, InceptionV3, NASNetLarge, and Xception—were applied to both datasets, and the outcomes were compared. The DenseNet201 model, when trained on augmented data, demonstrated remarkable performance in detecting monkeypox, achieving accuracy rates of 99.33% on the MSID dataset and 98.52% on the HYBRID dataset.
Keywords : Maymun Çiçeği Hastalığı, Virüs, Derin Öğrenme, Monkeypox, DenseNet201