Skin Disease Identification and Diagnosis Using  Deep Learning

Bharati Pannyagol; Sangeeta Udapudi; Madhavi Surpur; Rashmi Musale

Skin Disease Identification and Diagnosis Using Deep Learning

Bharati Pannyagol, Sangeeta Udapudi, Madhavi Surpur, Rashmi Musale

Abstract

Disorders of dermatological disease are one of the general diseases in the world. Its diagnosis is extremely difficult because of its complexities of skin tone, color, presence of hair etc. Our project provides an approach to use computer vision-based techniques to automatically predict the various kinds of skin diseases. The system uses are publicly available image recognition architectures namely Inception V3 or Inception Resnet V2 or Mobile Net with modifications for skin disease application and successfully predicts the skin disease based on maximum voting from the three networks. These models are pretrained to recognize images several classes like panda, parrot etc. which we will modify to predict skin diseases.. The system consists of three phases- The feature extraction phase, the training phase, and the testing /validation phase. The system makes use of deep learning technology to trained itself with the various skin images. The main objective of this system is to achieve maximum accuracy of skin disease.

Keywords

Computer vision, deep learning, CNN, object detection, inception V3, tensor flow

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References

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