# Deep Learning-Enhanced Spectrogram Analysis for
## 6 Conclusions  
This study introduced a deep learning classification framework for biological signal processing utilizing the ResNet50 model to categorize spectrograms derived from palpation and percussion across eight specific anatomical regions. The experimental results demonstrated a high classification accuracy of 93.37%, with minimal misclassification between neighboring regions, confirming the robustness and reliability of the model for automated biomedical signal classification. These findings highlight the clinical significance of deep learning in addressing the subjectivity inherent in traditional physical examination techniques, such as auscultation, percussion, and palpation. By providing an objective, reproducible, and automated classification system, this research contributes to the advancement of AI-driven diagnostic frameworks for non-invasive and remote healthcare applications. Despite these promising results, challenges remain, particularly in generalizing unseen patient data, reducing the computational complexity, and integrating multi-modal physiological data. Future research must prioritize real-time deployment, transfer learning for enhanced generalization, and explainable AI methodologies to increase the interpretability and acceptance of deep learning models in healthcare environments. This study confirmed the viability of spectrogram-based deep learning for biological signal classification and established a foundation for future progress in AI-assisted diagnostics. The proposed method offers a scalable and efficient solution that can be expanded to wider biomedical applications, thereby enhancing the advancement of automated clinical decision support systems. Unlike traditional approaches that focus on binary disease classification or participant-specific signal profiles, the proposed study presents an anatomically grounded classification model, an area that has not been addressed in previous deep learning frameworks. By integrating a spectrogram-based representation with a fine-tuned ResNet50 architecture, a classification accuracy of 93.37% was achieved across eight anatomical regions while preserving the spatial interpretability of the signal origin. To the best of our knowledge, this is the first study to classify biomedical signals derived from palpation and percussion into anatomical regions by using spectrogram analysis and a deep residual network. This original contribution presents a spatially aware diagnostic framework that expands the capabilities of deep learning in non-invasive clinical assessments. These results demonstrate the superiority of the proposed framework over previous methods, especially those limited to binary or participant-based classification, by achieving higher accuracy while preserving clinical interpretability through region-specific signal differentiation. Conceptualization, A.K. ; Methodology, A.K. ; Software, A.K. ; Validation, A.K., S.R. and I.C.J. ; formal analysis, A.K. ; investigation, A.K. ; resources, A.K. ; data curation, S.R. ; writing--original draft preparation, A.K. and S.R. ; writing--review and editing, I.C.J. ; visualization, A.K. ; supervision, I.C.J. ; project administration, I.C.J. ; funding acquisition, I.C.J. All authors have read and agreed to the published version of the manuscript. This research was funded by the Hallym University Research Fund, 2022 (HRF-202205-002). Not applicable. The datasets used and/or analyzed during the current study are not publicly available because of privacy and ethical restrictions but are available from the corresponding author, [PERSON] upon reasonable request. Requests to access the datasets should be directed at [EMAIL_ADDRESS]. **Acknowledgments:** The authors acknowledge Hallym University for providing the computing resources for model training and evaluation. Appreciation is expressed to the clinical staff at the university hospital for their assistance with data collection. **Conflicts of Interest:** The authors declare no conflicts of interest.