Skin diseases, one of the common human diseases, could be life-threatening if not diagnosed and treated early. This study proposes a skin disease detection model based on some image processing techniques and deep learning architectures. First, we deploy a data pre-processing procedure to convert the input images to Hue-Saturation-Value (HSV) color space and remove their unnecessary information with a Hanning Window-based filter. After applying the Hanning Window-based filter, we downsize the image to 64×64 before fetching it into the learning model. Next, we train the Convolutional Neural Network (CNN) model on the processed image dataset and the original image dataset to compare the effectiveness of two approaches. The experimental results show that using HSV color space and Hanning Window-based filter can improve the performance in diagnosing six out of eight considered types of skin diseases.