Monkeypox Disease Detection using Classical Backbone Models
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This project, under ICONLAB, aims to detect and distinguish monkeypox from smallpox, chickenpox, and measles. It focuses on raising awareness in the global medical imaging community. For accurate classification, we utilized deep learning backbone models such as VGG16, Inception Net, and ResNet.
The data for this study was collected from reputable sources like NCBI, as well as from datasets created by our own team. These models were employed to analyze medical images and identify unique features that differentiate monkeypox from other similar diseases, thereby enhancing diagnostic accuracy and contributing to better public health outcomes.