Convolution Neural Network for Feature Extraction in Skin Disease Detection

Seema Kolkur, Dhananjay Kalbande, Dr. Vidya Kharkar


Skin Diseases are becoming very common now days. Number of people suffering from skin diseases is increasing rapidly. Human judgment on diagnosis of skin diseases is sometimes subjective and not reproducible. To achieve more reliable and objective accuracy computer aided diagnosis may be used. With advancement in medical imaging, image based classification is been increasingly used for disease detection in medical field. Feature engineering is very important for any classifier to achieve maximum results. Convolution Neural Networks (CNN) can learn features on its own reducing total time required for development of such systems and at the same time increasing level of accuracy. We acquired more than 850 original images for two skin diseases from department of Skin and VD, KEM Hospital, Mumbai. We have used CNN for feature extraction from input images of two skin diseases. These features are fed to Support Vector Machine (SVM) for classification. The results indicate CNN can be feasibly used of feature extraction in skin disease detection.


Deep Learning, Convolution Neural Network

Full Text:



  • There are currently no refbacks.

Comments on this article

View all comments

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.