Brain Disease Diagnosis Prediction Model for Fuzzy Based Generic Shaped Clustering and HPU-Net

Authors

  • N. Kumaran Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai - 600062
  • I. Parvin Begum Department of Computer applications , B.S.Abdur Rahman Crescent Institute of Science and Technology , Vandalur, Tamil Nadu 600048, India
  • R. Ramani Department of Computer Science and Engineering, P.S.R Engineering college, Sivakasi, Tamil Nadu 626140
  • S. Pournima Department of Computer Science Engineering, Sona College of Technology, Salem, Tamil Nadu- 636005
  • D. Leela Rani Department of Electronics and Communication Engineering, School of Engineering, Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College), Tirupati-517102
  • A. Radhika Department of Electrical and Electronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore-641008, Tamil Nadu,India

Keywords:

Fussy system, brain image, image segmentation, Generic shaped Clustering algorithm, more transparent noise

Abstract

A current fuzzy logic based medical imaging rendering system for conferences on brain diseases. Lectures use MRI (Magnetic Resonance Imaging) images to present various noises and brittle boundaries, images based on artistic facts. Here the general pattern matching algorithm is improved. Brain imaging processing and brain disease prediction based on the Generic Shape Clustering (GSC) algorithm and HPU-NET (Hybrid Pyramid U-NET Model for Brain Tumor Segmentation) predict performance reliably. We collected a model image based on brain disease prediction from a Kaggle data set and simulated the prediction result and performance. The simulation results are compared with various predications-based algorithms. Then simulate the networks performance fast and the result performance based on mostly lower energy consumption and other model compared to stable changes.  Faster data transfer performance and overall network throughput. Basically faster 4.6 data transmission per second is better than other models. The future prediction performance reaches the maximum level of DSC accuracy and its classification accuracy is better than other models. To further validate the proposed system, CNN (convolutional neural network), RNN (recurrent neural network), FCM (fuzzy C-means), LDCFCM (local density clustering fuzzy C-means) are included in the simulation tests for comparison. The algorithm stops when the power consumption is determined. Results may provide evidence for feature recognition and predictive brain imaging diagnostics.

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Published

02.09.2023

How to Cite

Kumaran, N. ., Begum, I. P. ., Ramani, R. ., Pournima, S. ., Rani, D. L. ., & Radhika, A. . (2023). Brain Disease Diagnosis Prediction Model for Fuzzy Based Generic Shaped Clustering and HPU-Net. International Journal of Intelligent Systems and Applications in Engineering, 12(1s), 291–301. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3416

Issue

Vol. 12 No. 1s (2024)

Section

Research Article

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