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Stone accumulation in kidney is a typical disease/ sickness in most countries all around the world. Its frequency rate is continually expanding. It has been observed that, the classification of renal stones prompts an imperative decrease of the re-occurrence. The classification of stones based on particular texture, surface highlights and lab examinations are a standout amongst the most utilized strategies. In this paper we use dataset of explicitly intended for top captured pictures of 454 expelled kidney stones which extracted through urinary or surgical procedure for classification purpose. In this paper different techniques have been learned and applied the specialist’s defined framework to arrange them into defined classes then perform classification process. In this paper we use feature fusion technique to collect as much as possible features. We select VGG16, InceptionV3 and ALEX features for fusion using serial feature fusion method. We choose C-SVM and F-KNN classifier to get improved accuracy of same dataset and predict better correctness’s with the possibilities of expansion of the dataset measure. In initial testing classification accuracy recorded at 83.43%, FNR 19.25%, Precision Rate 88.48% and Sensitivity of 86.51% on CSVM, later on the best testing classification accuracy recorded at 99.5%, FNR 0.1%, Precision Rate 99.90% and Sensitivity of 99.96% on F-KNN.
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