A lung sound recognition model to diagnoses the respiratory diseases by using transfer learning

Lal, Nidhi

doi:10.1007/s11042-023-14727-0

Cited by 10 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, it computes the gradients of the target concept (i.e., the class output) with respect to the feature maps of the last convolutional layer. By pooling these gradients over the spatial dimensions, Grad-CAM produces a coarse localization map that highlights the parts of the image that have the greatest influence on CNN’s decision [ 34 , 38 ]. The architecture explaining the Grad-CAM technique is shown in Figure S2 .…”

Section: Resultsmentioning

confidence: 99%

“…Finally, the logarithm of the amplitude values (in dB) is taken to mimic the human ear’s logarithmic perception of loudness. The resulting LMS displays the frequency content in mels on one axis and time on the other, with the amplitude represented by a logarithmically scaled color map [ 38 ]. In this research, LMS representations were computed using 128 ms (2048 samples) window lengths and 32 ms (512 samples) hop lengths for the STFT, with examples provided in the referenced Figure 4 .…”

Section: Methodsmentioning

confidence: 99%

“…The table compares precision, recall, F1-score, and accuracy across models. References [38,57] are cited in the Supplementary Materials. Institutional Review Board Statement: Ethical review and approval were waived.…”

Section: Supplementary Materialsmentioning

confidence: 99%

“…The table compares precision, recall, F1-score, and accuracy across models. References [ 38 , 57 ] are cited in the Supplementary Materials.…”

mentioning

confidence: 99%

See 3 more Smart Citations

Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns

Malekroodi,

Madusanka,

Lee

et al. 2024

Bioengineering

View full text Add to dashboard Cite

Speech impairments often emerge as one of the primary indicators of Parkinson’s disease (PD), albeit not readily apparent in its early stages. While previous studies focused predominantly on binary PD detection, this research explored the use of deep learning models to automatically classify sustained vowel recordings into healthy controls, mild PD, or severe PD based on motor symptom severity scores. Popular convolutional neural network (CNN) architectures, VGG and ResNet, as well as vision transformers, Swin, were fine-tuned on log mel spectrogram image representations of the segmented voice data. Furthermore, the research investigated the effects of audio segment lengths and specific vowel sounds on the performance of these models. The findings indicated that implementing longer segments yielded better performance. The models showed strong capability in distinguishing PD from healthy subjects, achieving over 95% precision. However, reliably discriminating between mild and severe PD cases remained challenging. The VGG16 achieved the best overall classification performance with 91.8% accuracy and the largest area under the ROC curve. Furthermore, focusing analysis on the vowel /u/ could further improve accuracy to 96%. Applying visualization techniques like Grad-CAM also highlighted how CNN models focused on localized spectrogram regions while transformers attended to more widespread patterns. Overall, this work showed the potential of deep learning for non-invasive screening and monitoring of PD progression from voice recordings, but larger multi-class labeled datasets are needed to further improve severity classification.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Supplementary Materialsmentioning

confidence: 99%

“…The table compares precision, recall, F1-score, and accuracy across models. References [ 38 , 57 ] are cited in the Supplementary Materials.…”

mentioning

confidence: 99%

See 2 more Smart Citations