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Found 5 papers, 1 papers with code
Uformer: A UNet-Transformer fused robust end-to-end deep learning framework for real-time denoising of lung sounds
The proposed model showed an average SNR improvement of 16. 51 dB when evaluated with -12 dB LS signals.
Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation
We have performed in-depth analysis of different feature extraction techniques in order to find the most optimal one for OT classification and segmentation of lesions.
NRC-Net: Automated noise robust cardio net for detecting valvular cardiac diseases using optimum transformation method with heart sound signals
Therefore, this study aims to discover the optimal transformation method for detecting CVDs using noisy heart sound signals and propose a noise robust network to improve the CVDs classification performance. For the identification of the optimal transformation method for noisy heart sound data mel-frequency cepstral coefficients (MFCCs), short-time Fourier transform (STFT), constant-Q nonstationary Gabor transform (CQT) and continuous wavelet transform (CWT) has been used with VGG16.
An automated end-to-end deep learning-based framework for lung cancer diagnosis by detecting and classifying the lung nodules
The performance of both networks of the proposed framework was compared with other studies and found to outperform them regarding segmentation and detection accuracy.
A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogram
Listening to lung sounds through auscultation is vital in examining the respiratory system for abnormalities.
