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- Title
A new deep neuro-fuzzy system for Lyme disease detection and classification using UNet, Inception, and XGBoost model from medical images.
- Authors
Priyan, S. Vishnu; Dhanasekaran, S.; Karthick, P. Vivek; Silambarasan, D.
- Abstract
Lyme disease, caused by a bacterium transmitted through the bite of an infected tick, is often misdiagnosed due to its similarity to other conditions like drug rash. This research introduces an innovative approach by integrating prominent deep learning models, including UNet, Inception Model, and XGBoost, into the Deep Neuro-Fuzzy System. Utilizing a comprehensive Kaggle dataset, authors study aims to achieve heightened accuracy in recognizing and segmenting Lyme disease from medical images. Implemented in Python, authors advanced image processing methods demonstrate exceptional performance, reaching an outstanding accuracy of 97.36% after the recognition stage. To further enhance accuracy, authors introduce an additional layer of sophistication through the incorporation of the mayfly optimization (MO) approach. This strategic integration of MO contributes to the outstanding accuracy achieved by their models. This research not only addresses the challenges of Lyme disease misdiagnosis but also presents a robust framework for medical image recognition. Leveraging the collaborative and open nature of Kaggle and the versatility of the Python programming ecosystem, authors work contributes to advancing the field of Lyme disease detection and medical image processing.
- Subjects
LYME disease; NOSOLOGY; DIAGNOSTIC imaging; IMAGE processing; IMAGE recognition (Computer vision); DEEP learning
- Publication
Neural Computing & Applications, 2024, Vol 36, Issue 16, p9361
- ISSN
0941-0643
- Publication type
Article
- DOI
10.1007/s00521-024-09583-4