ArticleMedical & biological engineering & computing2017
Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach.
Article in Medical & biological engineering & computing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control.Journal of imaging · 2023Review
- Detection and Classification of Colorectal Polyp Using Deep Learning.BioMed research international · 2022Article
- Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos.Diagnostics (Basel, Switzerland) · 2021Article
- On Outsourcing Artificial Neural Network Learning of Privacy-Sensitive Medical Data to the Cloud.International Conference on Tools with Artificial Intelligence : [proceedings]. International Conference on Tools for Artificial Intelligence · 2021Article
- Status quo and future prospects of artificial neural network from the perspective of gastroenterologists.World journal of gastroenterology · 2021Review
- Application of improved fuzzy best worst analytic hierarchy process on renewable energy.PeerJ. Computer science · 2021Article
- Grey-Wolf-Based Wang's Demons for Retinal Image Registration.Entropy (Basel, Switzerland) · 2020Article
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- Applications of artificial neural networks in health care organizational decision-making: A scoping review.PloS one · 2019Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crohn's disease (CD) diagnosis is a tremendously serious health problem due to its ultimately effect on the gastrointestinal tract that leads to the need of complex medical assistance. In this study, the backpropagation neural network fuzzy classifier and a neuro-fuzzy model are combined for diagnosing the CD. Factor analysis is used for data dimension reduction. The effect on the system performance has been investigated when using fuzzy partitioning and dimension reduction. Additionally, further comparison is done between the different levels of the fuzzy partition to reach the optimal performance accuracy level. The performance evaluation of the proposed system is estimated using the classification accuracy and other metrics. The experimental results revealed that the classification with level-8 partitioning provides a classification accuracy of 97.67 %, with a sensitivity and specificity of 96.07 and 100 %, respectively.
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Registered trials
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