# Deep Learning-Enhanced Spectrogram Analysis for
## 4 Results
### Spectrogram-Based Analysis Across Anatomical Regions  
Spectrograms provide a powerful time-frequency representation of biomedical signals, capturing both the spectral and temporal characteristics essential for accurate classification. In this study, spectrograms derived from percussion and palpation signals were used to train the ResNet50 model to distinguish eight anatomical regions.  
The model effectively leveraged frequency-domain patterns across different spectral bands, enabling it to capture both the high- and low-frequency fluctuations unique to each region. These patterns, which are difficult to detect through manual observation or handcrafted features, were automatically extracted by the deep learning architecture, improving classification reliability.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Methodology** & **Dataset** & **Accuracy (\%)** & **Key Findings** & **Limitations** \\ \hline [PERSON] et al. [28] & CNN deep learning model with transfer learning & Cervical cancer diagnosis dataset & 94.2 & Demonstrated effectiveness of deep learning in medical imaging classification analysis \\  & Deep multi-modal learning with ausculation, percussion, and palpation & Collected multi-modal dataset (8 regions) & 89.46 & Achieved high accuracy in classifying abnormal divisions & Requires further testing on unseen patient data \\  & Multi-time-scale feature extraction for respiratory sound classification & Publicly available respiratory sound classification & 91.3 & Improved feature representation for robust respiratory disease classification & Dependence on high-quality labeled datasets for training Demonstrated \\  & Scalogram-based CNN for respiratory dataset classification & Respiratory sound dataset from clinical sources & 92.5 & Calogram-based feature extraction for classification & Limited generalization for unseen respiratory conditions for classification \\  & Analysis of adventitious lung sounds for tuberculosis detection & Clinical lung sound recordings from TB patients & 87.8 & Effective identification of TB-related acoustic anomalies & Requires further validation with diverse population groups \\  & Fine-tuned ResNet50 on spectrogram dataset (8 anatomical regions) & Spectrogram dataset feature extraction & 93.37 & High classification accuracy, robust feature extraction & Requires further testing on unseen patient data \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of Biomedical Signal Classification Methods.  
To illustrate the distinct spectral signatures among anatomical regions, a set of representative spectrograms from multiple locations and subjects was provided (see Figure 6). These variations reveal the presence of overlapping frequency characteristics in neighboring areas, which contribute to classification difficulty. Despite these challenges, the model demonstrated a strong regional sensitivity and consistently detected subtle spectral changes.  
This analysis highlights the strength of spectrogram-based deep learning in biomedical signal processing, particularly for non-invasive diagnostic applications involving ausculation, percussion, and palpation. The proposed approach enables spatially contextualized signal classification and improves the precision of the anatomical interpretation.