Özet:

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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