Real-Time Object Detector for Medical Diagnostics (RTMDet): A High-Performance Deep Learning Model for Brain Tumor Diagnosis

Bioengineering, Journal Year: 2025, Volume and Issue: 12(3), P. 274 - 274

Published: March 11, 2025

Background: Brain tumor diagnosis requires precise and timely detection, which directly impacts treatment decisions patient outcomes. The integration of deep learning technologies in medical diagnostics has improved the accuracy efficiency these processes, yet real-time processing remains a challenge due to computational intensity current models. This study introduces Real-Time Object Detector for Medical Diagnostics (RTMDet), aims address limitations by optimizing convolutional neural network (CNN) architectures enhanced speed accuracy. Methods: RTMDet model incorporates novel depthwise blocks designed reduce load while maintaining diagnostic precision. effectiveness was evaluated through extensive testing against traditional modern CNN using comprehensive imaging datasets, with focus on capabilities. Results: demonstrated superior performance detecting brain tumors, achieving higher compared existing model’s validated its ability process large datasets real time without sacrificing required reliable diagnosis. Conclusions: represents significant advancement application diagnostics. By balance between precision, enhances capabilities imaging, potentially improving outcomes faster more accurate detection. offers promising solution clinical settings where rapid are critical.

Language: Английский

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MicrobeNet: An Automated Approach for Microbe Organisms Prediction Using Feature Fusion and Weighted CNN Model

International Journal of Computational Intelligence Systems, Journal Year: 2025, Volume and Issue: 18(1)

Published: March 13, 2025

Language: Английский

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Multi-level feature fusion network for kidney disease detection

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 191, P. 110214 - 110214

Published: April 14, 2025

Language: Английский

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Enhancing skin lesion classification: a CNN approach with human baseline comparison

PeerJ Computer Science, Journal Year: 2025, Volume and Issue: 11, P. e2795 - e2795

Published: April 15, 2025

This study presents an augmented hybrid approach for improving the diagnosis of malignant skin lesions by combining convolutional neural network (CNN) predictions with selective human interventions based on prediction confidence. The algorithm retains high-confidence CNN while replacing low-confidence outputs expert assessments to enhance diagnostic accuracy. A model utilizing EfficientNetB3 backbone is trained datasets from ISIC-2019 and ISIC-2020 SIIM-ISIC melanoma classification challenges evaluated a 150-image test set. model’s are compared against 69 experienced medical professionals. Performance assessed using receiver operating characteristic (ROC) curves area under curve (AUC) metrics, alongside analysis resource costs. baseline achieves AUC 0.822, slightly below performance experts. However, improves true positive rate 0.782 reduces false 0.182, delivering better minimal involvement. offers scalable, resource-efficient solution address variability in image analysis, effectively harnessing complementary strengths humans CNNs.

Language: Английский

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Detection of Abnormal Cardiac Rhythms Using Feature Fusion Technique with Heart Sound Spectrograms

Journal of Bionic Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Language: Английский

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