An Optimal Texture Pattern Model of Big Data Processing for Canine Disease Classification


  • Ayesha Taranum Research Scholar, Atria Institute of Technology, Visvesvaraya Technological University, Belagavi – 590018, India,
  • Shanthi Mahesh Professor and Head, Atria Institute of Technology, Visvesvaraya Technological University, Belagavi – 590018, India


Big Data Analysis, Big Data processing, Image Classification, Intensity Distributional Texture Pattern (IDTP), Neural Network (NN), Texture Feature Model


In the field of image processing, there are several varieties of data prediction models, each of which can identify the kind of data and determine how the data will be labelled. When compared to the other feature representation approaches, the texture pattern recognition method helps enhance prediction performance. As a result of the fact that this method of image texture feature extraction increases the amount of the features for the pictures, big data processing is achieved. In this study, an optimum image classification model for the canine image dataset was suggested to cope with the large amounts of data. During this process, the picture was analysed to determine the many skin diseases that canine slides had. Utilizing the Intensity Distributional Texture Pattern, we were able to successfully extract the texture pattern for the picture (IDTP). This represents the picture texture pattern by implementing the distribution function for the intensity of image matrix. As a result of this, the picture was categorized using the neural network model, and the performance of the suggested model was validated. The findings and the comparative analysis show that the performance of the suggested model is improved in comparison to the performance of the other conventional classification and prediction approaches.


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How to Cite

Taranum, A. ., & Mahesh, S. . (2023). An Optimal Texture Pattern Model of Big Data Processing for Canine Disease Classification. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 458–466. Retrieved from



Research Article