A review of brain tumour detection through the integration of fuzzy logic and U-NET CNN classification: A high-efficiency approach

Brain tumor detection is critical to early and accurate diagnosis, leading to improved patient outcomes. This review article explores the fusion of Fuzzy Logic and U-Net Convolutional Neural Networks (CNNs) as a groundbreaking approach to enhance brain tumor detection accuracy. Fuzzy logic addresses...

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Bibliographic Details
Main Authors: Deshmukh, Santoshi, Kumar, Praveen, Devangan, Leelkanth, Patil, A. R. Bhagat
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Artificial neural networks
Brain
Classification
Clustering
Diagnosis
Fuzzy logic
Image analysis
Image enhancement
Medical imaging
Tumors
Uncertainty analysis
Online Access:Get full text
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Summary:Brain tumor detection is critical to early and accurate diagnosis, leading to improved patient outcomes. This review article explores the fusion of Fuzzy Logic and U-Net Convolutional Neural Networks (CNNs) as a groundbreaking approach to enhance brain tumor detection accuracy. Fuzzy logic addresses uncertainty and imprecision in medical image analysis, offering nuanced membership functions, rule-based systems, and clustering algorithms. U-Net CNNs excel in segmenting complex medical images. The collaboration of Fuzzy Logic and U-Net CNNs promises to overcome tumor boundary delineation and classification challenges, thereby advancing brain tumor detection methodologies. This review delves into this hybrid approach’s theoretical underpinnings, challenges, and future prospects, offering insights into its potential to revolutionize brain tumor diagnosis.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0240766