Digital Technologies Research and Applications

Article

Hybridized Deep Neural Network Using Adaptive Rain Optimizer Algorithm for Multi-Grade Brain Tumor Classification of MRI Images

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https://doi.org/10.54963/dtra.v1i1.28
Sasank, V. V. S., & Venkateswarlu, S. (2022). Hybridized Deep Neural Network Using Adaptive Rain Optimizer Algorithm for Multi-Grade Brain Tumor Classification of MRI Images. Digital Technologies Research and Applications, 1(1), 13–30. https://doi.org/10.54963/dtra.v1i1.28
Volume 1 Issue 1: March 2022
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Authors

  • V. V. S. Sasank
    Department of Computer Science and Engineering, KoneruLakshmaiah Education Foundation, Vaddeswaram, AP, 522502, India
  • S. Venkateswarlu Department of Computer Science and Engineering, KoneruLakshmaiah Education Foundation, Vaddeswaram, AP, 522502, India

Classification of brain tumor is highly significant in the medical field in real-world to improve the progress of treatments. The seriousness behind the tumors are normally graded based on the size into grade I, grade II, grade III and grade IV. This is where the process of multi-grade brain tumor classification gains attention. Thus, the article focusses on classifying the brain MRI images into four different grades by proposing a novel and a very efficient classification strategy with high accuracy. The acquired images are pre-processed with the help of an Extended Adaptive Wiener Filter (EAWF) and then segmented using the piecewise Fuzzy C- means Clustering (piFCM) technique. Then the most ideal features such as the texture, intensity and shape features that can best explain the growth of tumors are extracted using the Local Binary Pattern (LBP) and the Hybrid Local Directional Pattern with Gabor Filter (HLDP-GF) techniques. After extracting the ideal features, the Manta Ray Foraging Optimization (MRFO) method has been introduced to optimally select the most relevant features. Finally, a Hybrid Deep Neural Network with Adaptive Rain Optimizer Algorithm (HDNN- AROA) is proposed to classify the grades of brain tumors with high accuracy and efficiency. The proposed technique has been compared with the existing state-of-the-art techniques relevant to brain tumor classification in terms of accuracy, precision, recall and dice similarity coefficient to prove the overall efficiency of the system.

Keywords:

Multi-grade brain tumor classification Segmentation Feature extraction Feature selection Classification

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