Skin Disease Detection Using Image Processing & CNN

Dermatological diseases are the most prevalent diseases worldwide. Even though being common, diagnosis is extremely difficult and requires extensive experience in the domain. In this project, we provide an approach to detect various kinds of these diseases. Computer vision and Machine learning are dual stages that we used to identify diseases accurately. Our objective of the project is to detect the type of skin disease easily with accuracy and recommend the best. In the first stage of the image the skin disease is subject to various kinds of pre-processing techniques followed by feature extraction. Then the second stage involves it uses Machine learning algorithms to identify diseases based on the analyzing and observance of the skin. The proposed system is highly beneficial in rural areas where access to dermatologists is limited. For this proposed system, we use Pycharm based python script for experimental results.

Skin types of diseases are most common among the globe, as people get skin disease due to inheritance, environmental factors. In many cases people ignore the impact of skin disease at the early stage. In the existing system, the skin disease are identified using biopsy process which is analyzed and medicinal prescribed manually by the physicians. To overcome this manual inspection and provide promising results in short period of time, we propose a hybrid approach combining computer vision and machine learning techniques. For this, the input images would be microscopic images i.e histopathological from which features like color, shape, and texture are extracted and given to a convolutional neural network (CNN) for classification and disease identification. Our objective of the project is to detect the type of skin disease easily with accuracy and recommend the best and global medical suggestions. skin disease much more quickly and accurately. But the cost of such a diagnosis is still limited and very expensive. So, image processing techniques help to build an automated screening system for dermatology at an initial stage. The extraction of features plays a key role in helping to classify skin diseases. Computer vision has a role in the detection of skin diseases in a variety of techniques. Due to deserts and hot weather, skin diseases are common in Saudi Arabia. This work contributes in the research of skin disease detection. We proposed an image processing-based method to detect skin diseases

Keyword: Ham10000, image processing, CNN, ResNet50, Xception, Skin Disease Classification.