Automatic Monkeypox Disease Detection from Preprocessed Images using MobileNetV2

The prevalence of infectious diseases in humankind is increasing globally due to a variety of reasons, and accurate diagnosis and treatment will help control/cure the disease. A severe health problem is associated with Monkeypox (Mpox), a communicable illness caused by the monkeypox virus. This research aims to develop a computerized tool to detect Mpox from pre-processed images using the pre-trained lightweight deep-learning scheme (PLDS). This tool consists of the following phases; (i) Image collection and tri-level thresholding based pre-processing, (ii) Feature extraction using selected PLDS, and (iii) five-fold cross-validation supported binary classification. As part of this research, we examine the possibilities for developing an accurate Mpox detection system using PLDS, including; (i) Kapur's thresholding, (ii) chosen optimizers, and (ii) chosen activation functions. This experimental investigation utilizes augmented images from the Monkeypox Skin Images Dataset (MSID), and the developed tool with MobileNetV2 achieves 98.7% detection accuracy when Kapur's thresholding is applied. Further, this tool presents a testing accuracy of >90% on the original MSID images, confirming the proposed research's significance.