Skin diseases are common in human beings because of significant changes in surrounding environments. The most of these diseases are curable if diagnosed at initial stages. Therefore, early diagnosis can spare people's precious lives. To address these issues, we proposed a novel model based on deep learning to diagnose the skin disease at a preliminary stage using classification. The developed model correctly identifies six different skin diseases namely, actinic keratosis, benign keratosis, melanoma, basal cell carcinoma, insects bite and skin acne. Several stateof-the-art algorithms are examined on benchmark datasets (International Skin Imaging Collaboration (ISIC) 2019 dataset and UCI Data Center) for accuracy, precision, recall and F1-score metrics. The results show that convolutional neural network (CNN) has a distinct superiority over its peers with accuracy rate of97%, precision 91%, recall 91% and F1-score 91%. This system will provide skin care handling services that are precise and accurate and help the dermatologist in early diagnosis of skin diseases.