A new deep neuro-fuzzy system for Lyme disease detection and classification using UNet, Inception, and XGBoost model from medical images

Priyan, S. Vishnu; Dhanasekaran, S.; Karthick, P. Vivek; Silambarasan, D.

doi:10.1007/s00521-024-09583-4

Neural Comput & Applic

2024

DOI: 10.1007/s00521-024-09583-4

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A new deep neuro-fuzzy system for Lyme disease detection and classification using UNet, Inception, and XGBoost model from medical images

S. Vishnu Priyan,

S. Dhanasekaran,

P. Vivek Karthick

et al.

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2024

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Cited by 2 publications

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ResDense Fusion: enhancing schizophrenia disorder detection in EEG data through ensemble fusion of deep learning models

Senthil Kumar,

Venmathi,

Thangavel

et al. 2024

Neural Comput & Applic

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ResDense Fusion: enhancing schizophrenia disorder detection in EEG data through ensemble fusion of deep learning models

Senthil Kumar,

Venmathi,

Thangavel

et al. 2024

Neural Comput & Applic

View full text Add to dashboard Cite

Bonevoyage: Navigating the depths of osteoporosis detection with a dual-core ensemble of cascaded ShuffleNet and neural networks

Srinivasan,

Kiran,

Parameswari

et al. 2024

Journal of X-Ray Science and Technology: Clinical Applications

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Background Osteoporosis (OP) is a condition that significantly decreases bone density and strength, often remaining undetected until the occurrence of a fracture. Timely identification of OP is essential for preventing fractures, reducing morbidity, and enhancing the quality of life. Objective This study aims to improve the accuracy, speed, and reliability of early-stage osteoporosis detection by integrating the compact architecture of Cascaded ShuffleNet with the pattern recognition prowess of Artificial Neural Networks (ANNs). Methods BoneVoyage leverages the efficiency of ShuffleNet and the analytical capabilities of ANNs to extract and analyze complex features from bone density scans. The framework was trained and validated on a comprehensive dataset containing thousands of bone density images, ensuring robustness across diverse cases. Results This model achieving an accuracy of 97.2%, with high sensitivity and specificity. These results significantly surpass those of existing OP detection methods, highlighting the effectiveness of the BoneVoyage framework in identifying subtle changes in bone density indicative of early-stage osteoporosis. Conclusions BoneVoyage represents a significant advancement in the early detection of osteoporosis, offering a reliable tool for healthcare providers to identify at-risk individuals prematurely. The early detection facilitated by BoneVoyage allows for the implementation of preventive measures and targeted treatments.

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A new deep neuro-fuzzy system for Lyme disease detection and classification using UNet, Inception, and XGBoost model from medical images

Cited by 2 publications

References 20 publications

ResDense Fusion: enhancing schizophrenia disorder detection in EEG data through ensemble fusion of deep learning models

ResDense Fusion: enhancing schizophrenia disorder detection in EEG data through ensemble fusion of deep learning models

Bonevoyage: Navigating the depths of osteoporosis detection with a dual-core ensemble of cascaded ShuffleNet and neural networks

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