Abstract
Dermoscopy is one of the most irregular and challenging areas to diagnose as it is very complex. In the sphere of dermatology, numerous numbers of times, thorough examinations are required to be carried out to resolve upon the skin ailment the patient may be facing. Different practitioners may take a different amount of time to detect the skin disease. So, a system is required that can efficiently and accurately diagnose the skin conditions without any such restrictions. This paper presents an automated dermatological diagnostic system using a deep learning approach. Dermatology is the branch of medicine which deals with the identification and treatment of skin diseases. The presented system is a machine interference in contradiction to the traditional medical personnel-based belief of dermatological diagnosis. The entire system works on the two mutually dependent steps. The first is preprocessing of image of that part of skin that is infected and the second step is used to recognize the disease. The system uses convolutional neural networks and feedforward backpropagation for the identification of skin disease. The system gives an accuracy of 93.063% while testing on a total of 180 image samples for six disease classes.
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Acknowledgements
We would like to thank our guide Mr. Debabrath Swain for helping us with his invaluable experience and also his constant motivation has helped us complete our project successfully.
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Swain, D., Bijawe, S., Akolkar, P., Mahajani, M., Shinde, A., Maladhari, P. (2020). Virtual Dermoscopy Using Deep Learning Approach. In: Mallick, P., Pattnaik, P., Panda, A., Balas, V. (eds) Cognitive Computing in Human Cognition. Learning and Analytics in Intelligent Systems, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-030-48118-6_6
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DOI: https://doi.org/10.1007/978-3-030-48118-6_6
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