The recent monkeypox outbreak has become a public health concern due to its rapid spread in more than 40 countries outside Africa. Early diagnosis of monkeypox is challenging due to its similarity to chickenpox and measles. The use of computer-assisted detection of monkeypox lesions could prove beneficial for the surveillance and rapid identification of suspected cases. If sufficient training examples are available, deep learning methods have been found to be effec- tive in the automated detection of skin lesions. To improve the diagnosis of monkeypox using mobile applications, we utilized MobileNet and ShuffleNet which are types of Fully Connected Convolutional Neural Networks.