| Sumario: | In these days, the use of machine learning and internet has occupied a large space in the field of Bio medical Technology. The machine learning is capable of make decisions based on the past data and it will used for many disease identification and treatment. It has changed our way of health monitoring, diagnosis the disease and its level. In this proposal we use the Machine learning to acquire data from dermoscopy image and from that we diagnosis the impact of disease and monitor it on realtime process. Deep learning methods such as deep convolutional neural networks (CNNs) have established an overwhelming presence in image recognition tasks in the past few years. The main advantage of CNN is that it is endowed with an impressive visual representation capability for the recognition or detection task depending on the given training dataset. We present our proposed framework in details. Here the deep residual neural network is applied in our method, followed by the extraction of local dense activations as deep convolutional features in our framework. Then, FV (fisher vector) encoding strategy is utilized to aggregate these deep features for more discriminative and robust representations. Finally, the classification method of the FV representations is present. Then the result is updated to a webserver by using IOT. Based on the data received from the MATLAB we will indicate the types of disease.
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