# A Deep Learning-Based Mobile Application for Monkeypox Detection
### Classification Approaches  
There are many researchers who classified similar skin diseases before discovering monkeypox disease. [PERSON] et al. [15] approach is based on Support Vector Machines (SVM), and therefore three skin diseases were classified. Dermatitis, herpes, and psoriasis. [PERSON] et al. [16] employed convolutional neural networks (CNN) associated with five distinct ML classifiers to identify three kinds of skin diseases (i.e., acne, lichen planus and sjs ten). Furthermore, [PERSON] et al. [17] in their research developed three types of learning models: transfer learning using the existing AlexNet architecture (Alexnet-TL), SVM modeling based on feature extraction data from images (FESVM), and SVM modeling based on feature extraction data from images and patient data (FVM). [PERSON] et al. [18] utilized different segmentation approaches to detect skin diseases such as chickenpox. In their work, they applied different image processing techniques such as adaptive thresholding, edge detection, K-means clustering, and morphology-based image segmentation. The earliest trials to detect other skin diseases similar to monkeypox were conducted by [PERSON] et al. [19]. They used some pre-trained DL models on the MSLD dataset and discovered that the MobileNetV2 and EfficientNetB0 models performed well. [PERSON] et al. [20] developed a DL model to detect varicella zoster using K-Nearest Neighborhood (KNN), neural networks, and logistic regression. [PERSON] et al. [21], proposed a smartphone-based skin disease-detection method utilizing MobileNetV2. In addition, through the data presented in [PERSON]'s work [22], they evaluated the performance of ResNet50, DenseNet121, Inception-V3, SqueezeNet, MnasNetA1, MobileNet-V2, and ShuffleNet-V2. [PERSON] et al. [23] analyzed monkeyox skin lesions using an AI-assisted CNN. ResNet-18, ResNet-50, VGG-16, Densenet-161, EfficientNet B7, EfficientNet V2, GoogLeNet, MobileNet V2, MobileNet V3, ResNeXt-50, ShuffleNetV2, and ConvNeXt were used in the experiments. This resulted in the MobileNetV2 model having the best accuracy compared with the other modules. [PERSON] et al. [13] proposed a web-scraping-based data collection system for a monkeyox skin lesion. In the classification task, ResNet50, Inception-V3, DenseNet121, MnasNet-A1, MobileNet-V2, ShuffleNet-V2, and SqueezeNet models were used. The ShuffleNet-V2 outperformed the other modules. [PERSON] et al. [24] generated a new monkeyox classification dataset. Based on the 1915 augmented images, the VGG16 model was implemented. [PERSON] et al. [10] applied different deep learning modules to the MSID dataset in order to classify monkeyox. These modules included VGG16, ResNet50, Inception-V3, and ResNet50. The ResNet50 achieved the best accuracy compared to other modules. [PERSON] et al. [21] presented an android mobile application, using a smartphone camera to detect and classify the monkey-box. They used four different DL networks VGG16, ResNet50, InceptionV3, and Ensemble. In their experiment, EfficientNet0 and MobileNetv2 showed better performance in terms of accuracy compared to other modules. [PERSON] et al. [25] applied the MSLD dataset to different transfer-learning-based models using VGG19, DenseNet121, Xception, EfficientNetB3, and MobileNetV2. [PERSON] et al. [10] analyzed monkeyox skin lesions and other diseases (chickenpox, measles) using pre-trained models such as VGG-16, ResNet50, and InceptionV3. On another hand, [PERSON] et al. [22] compared the performance of monkeyox classification using wide range of modules: ResNet50, DenseNet121, Inception-V3, SqueezeNet, MnasNet-A1, MobileNet-V2 and ShuffleNet-V2. [PERSON] et al. [9] applied a modified VGG16 to identify monkeyox. They applied feature extraction and prediction using Local Interpretable Model-Agnostic Explanations (LIME). [PERSON] et al. [11] introduced a new method based on DenseNet-169 and Xception on the monkeyox-2022 image dataset, whereas [PERSON] et al. [26] utilized DenseNet-161, ResNet-152, NASNet, and SE ResNeXt-101 in the DermNet dataset.