Skin diseases are reported to be the most common disease in humans among all age groups and a major cause of illness in sub-Saharan Africa. However, diagnosis and treatment of
skin disease are seen to be difficult, due to the orthodox approaches used by many medical centers globally. In recent times, artificial intelligence has been applied to enhance computer vision applications to permit easy detection of patterns in images. Notwithstanding this breakthrough in technology, the dermatological process in Ghana is yet to be automated, making the diagnosis of skin disease difficult and time-consuming. The current study sought to develop a web-based skin disease detection system (Medilab-Plus), which allows an online user to detect skin diseases in human and to make available, advises or possible medical actions in a precise short period. A convolutional neural network classifier built upon a Tensor flow framework for classifying a user-uploaded image as Eczema, Impetigo or Melanoma. Experimental results of the proposed system exhibit disease identification accuracy of 88% for Atopic dermatitis, 85% for Acne vulgaris and 84.7% for Scabies.