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@article{154162,
        author = {Neethu C Sekhar and Rosmin Augustine  and Johny Xavier  Fernandez  and Midhlaj Ahammed T K and Serene Anna Giji},
        title = {Derma-Speziale: An Image-Based  Automated System for Skin Disease Identification Using Convolutional Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {7},
        pages = {1-6},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154162},
        abstract = {Skin  disease  is perhaps the  most  well-known  kind of human  illness, which may happen  to everybody  regardless of any demographic characteristics and skin diseases are becoming one of the most common health issues in all countries  worldwide. Multiple tests should be carried out to determine  the skin diseases faced by patients.  This takes a while, depending  on the prediction of the  diagnosis.  As a result, a framework is required that  can analyse  skin  diseases  without  any  of  these  requirements and provide  superior  results in seconds. An automated image-based system based  on convolutional  neural  networks  (CNN) for  skin disease recognition is proposed  in this paper.  Training dataset  is required for various skin diseases.. The dataset  includes all forms of skin diseases, however we focused on nine main  types of skin diseases, with each class including between 150 and 300 samples. Users can enter images and system processes, use CNN algorithm to extract features, and use softmax classifier to diagnose diseases. The proposed  CNN model is compared  with a recurrent neural networks(RNN)  model to ensure  CNN model is more accurate  in classification and  prediction  of the input  images.},
        keywords = {Deep Learning, prediction  modelling, CNN, Tensorflow},
        month = {},
        }