Optimal Technique of Tumor Detection and Prediction of Livestock by Deep Neural Network with TensorFlow and Keras

Abstract

–In this paper, we emphasis on the method by which a sick livestock can be diagnosed of the probable infections and predict the type of disease. Proposed an approach to distinguish whether an MRI picture of a brain contains a possible tumor of livestock. Designed a computer-aided detection approach to detect a brain tumor in its early stage by using deep neural network using Keras and Tensor flow. The main problem faced by a farmer/livestock owner is that ofthe geographical distances of the sick animal from the healthcenter or the doctors who can treat and suggest the possible cure. By leveraging the modern technology in application and developments in Machine Learning and IOT technology the above-mentioned problem can be addressed as of the optimal approach for the farmer. The Detection of tumor is first predicted by Convolution Neural Network based Deep neural network using Keras and Tensorflow, followed is by which the MRI image is pre-processed to isolate the noise and any artefacts. The results are carried out by proposed method which can communicate directly to the cattle farmer using IOT. However the resultant of the computer aided process will automate the detection of diseas by which the farmers can directly know whether the cattle got effected with tumor or not. Time complexity can be significantly reduced with the proposed method. Eventually, computer aided system will assist the radiologist and the doctor in concluding of any illness on the livestock.

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