Fuzzy System Based Medical Image Processing for Brain Disease Prediction

• Published in: Frontiers in neuroscience Vol. 15; p. 714318
• Main Authors: Hu, Mandong, Zhong, Yi, Xie, Shuxuan, Lv, Haibin, Lv, Zhihan
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 30.07.2021; Frontiers Media S.A
• Subjects: Algorithms; Artificial intelligence; Brain cancer; Brain diseases; brain image; Brain tumors; Classification; Closed loop systems; Coronaviruses; COVID-19; Decision making; Decision trees; Diagnosis; digital twins; Disease transmission; fuzzy clustering; fuzzy system; HPU-Net; Image processing; image segmentation; Internet of Things; Magnetic resonance imaging; Medical imaging; Neural networks; Neuroimaging; Neuroscience; NMR; Nuclear magnetic resonance; Oral cancer; Prediction models; Segmentation; Surgery
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2021.714318

The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. A brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation) to ensure the model safety performance. Brain MRI images collected from a Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrate that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 s on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under DSC (Dice Similarity Coefficient), reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. In a word, the proposed algorithm can provide higher accuracy, a more apparent denoising effect, and the best segmentation and recognition effect than other models while ensuring energy consumption. The results can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.