# Deep Learning-Enhanced Spectrogram Analysis for
## 5 Discussion
### Comparison with Existing Deep Learning Approaches  
Several recent studies have explored deep learning for biomedical signal analysis, focusing predominantly on lung-sound classification and heart sound analysis [45; 46]. However, few models address the classification of palpation- and percussion-based spectrograms, which are critical components of physical examinations. Previous models, such as those relying on RNNs and LSTMs have exhibited limitations in handling spatial dependencies in biomedical spectrograms [47]. In contrast, the proposed ResNet50 model efficiently captured both local and global spectro temporal features, allowing for improved classification of biomedical signals from different anatomical regions. A comparison with existing deep learning-based classification methods was conducted to further substantiate the effectiveness of the proposed model. Table 4 provides an overview of recent biomedical signal classification studies and summarizes their methodologies, datasets, accuracy, key findings, and limitations. The proposed model surpasses multiple previous studies, attaining a classification accuracy of 93.37%, which is significantly higher than those of [PERSON] et al. [31] (89.46%) and [PERSON] et al. [35] (91.3%). This demonstrates the effectiveness of fine-tuning ResNet50 for spectrogram representations of biomedical signals. Although [PERSON] et al. [28] reported a higher accuracy of 94.2%, their study focused on cervical cancer imaging rather than on biomedical sound classification. Because the domain and data characteristics differ, a direct comparison is not possible. Unlike earlier studies that focused on disease classification (e.g., differentiating pathological from non pathological lung sounds), this study proposes a framework that classifies signals according to their anatomical origin-based classification strategy, ensuring that biomedical signals are analyzed within their spatial context. The confusion matrix (Figure 5) further supports the robustness of the proposed model by showing the minimal misclassification between adjacent regions [48]. This distinction is critical because anatomical region-based classification allows for finer granularity of analysis, making it useful for targeted medical interventions. By classifying signals based on anatomical regions rather than disease conditions, this approach ensures that diagnostic assessments remain independent of predefined pathological states that can vary significantly among patients. Table 4 further demonstrates the advantages of the proposed method, highlighting its ability to classify spectrogram-based biomedical signals with higher accuracy and lower misclassification rates compared to existing approaches [49]. By classifying biomedical signals based on anatomical regions rather than specific diseases, the proposed method enhances the diagnostic precision while remaining adaptable to multiple clinical applications. The integration of AI-based analysis in digital stethoscopes or wearable biosensors could further extend their usability in point-of-care diagnostics and home healthcare. Additionally, anatomical region-based classification provides several advantages over disease classification:  
* More precise localization of abnormal signals, aiding in targeted diagnosis and treatment planning. * Objective assessment of signal variability across different anatomical locations reduces inter-observer variability in manual auscultation [44]. * Potential integration with multi-modal diagnostic tools, where regional classification can enhance disease identification by correlating spatial signal variations with pathological findings [45]. Furthermore, while previous studies on disease classification rely heavily on predefined pathological patterns and large labeled datasets, anatomical region-based classification leverages spatial feature learning, making it adaptable to various diagnostic contexts.