# A Deep Learning-Based Mobile Application for Monkeypox Detection
###### Abstract  
The recent outbreak of monkeypox has raised significant concerns in the field of public health, primarily because it has quickly spread to over 40 countries outside of Africa. Detecting monkeypox in its early stages can be quite challenging because its symptoms can resemble those of chickenpox and measles. However, there is hope that potential use of computer-assisted tools may be used to identify monkeypox cases rapidly and efficiently. A promising approach involves the use of technology, specifically deep learning methods, which have proven effective in automatically detecting skin lesions when sufficient training examples are available. To improve monkeypox diagnosis through mobile applications, we have employed a particular neural network called MobileNetV2, which falls under the category of Fully Connected Convolutional Neural Networks (FCCNN). It enables us to identify suspected monkeypox cases accurately compared to classical machine learning approaches. The proposed approach was evaluated using the recall, precision, F score, and accuracy. The experimental results show that our architecture achieves an accuracy of 0.99%, a Recall of 1.0%, an F-score of 0.98%, and a Precision of 0.95%. We believe that such experimental evaluation will contribute to the medical domain and many use cases. artificial neural network; deep learning; detection performance; image analysis; image classification; machine learning models; neural network; object detectors +
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as it typically requires direct interaction with an infected individual. This limited spread resulted in the disease being confined to specific regions where such interactions occur regularly. However, recent outbreaks have shown an alarming increase in the number of monkeypx cases in countries where it was not previously considered endemic [5]. These outbreaks have highlighted the urgent need for early detection and accurate diagnosis of monkeypox infections [6]. Monkeypox is a typically self-limiting disease. A monkeypox rash appears between 1 and 5 days after the onset of the initial signs of monkeypox. The severity of the disease depends on the amount of virus exposure the patient has received, their health status, and any complications that occur. As a result, the disease spreads to other parts of the body. It may be mistaken for chickenpox due to the similarity of the rashes. Eventually, the rash becomes crusty and begins to spread, making it important to accurately detect and diagnose monkeypox early on [7; 8]. The symptoms of monkeypox, smallpox, and measles are almost identical, as shown in Figure 1, making it difficult to distinguish them without a laboratory test [9]. Vaccination is the only way to completely eradicate the disease [10]. While the virus is not life-threatening, it can cause complications in severe cases, such as sepsis, pneumonia, and blindness [9]. Based on research on virus-related disease detection using Deep Learning (DL) methods, the majority of studies use the transfer learning approach, which uses well-established pre-trained DL methods. To the best of our knowledge, there is no extensive research available on the detection of the monkeypox virus except for the work in ref. [9]. In ref. [9], interesting results have been achieved. It was determined that the state of the art has three major limitations. * Most of the models are limited to binary classification with limited performance. * There is a limited number of models that offer high accuracy in classifying monkeypox for quick, real-time diagnosis using smartphones. * Most of the models have been evaluated using some evaluation metrics in the literature. The best-performing models in the literature cannot be compared with others and are not sufficiently generalized, as in ref. [11]. A limited number of research studies have been found to demonstrate the potential of Machine Learning (ML) methods in diagnosing monkeypox disease using image processing techniques.