Deep Learning-Enhanced Spectrogram Analysis for Anatomical Region Classification in Biomedical Signals

Accurate classification of biomedical signals is essential for advancing non-invasive diagnostic techniques and improving clinical decision-making. This study introduces a deep learning-augmented spectrogram analysis framework for classifying biomedical signals into eight anatomically distinct regions, thereby addressing a significant deficiency in automated signal interpretation. The proposed approach leverages a fine-tuned ResNet50 model, pre-trained on ImageNet, and adapted for a single-channel spectrogram input to ensure robust feature extraction and high classification accuracy. Spectrograms derived from palpation and percussion signals were preprocessed into grayscale images and optimized through data augmentation and hyperparameter tuning to enhance the model’s generalization. The experimental results demonstrate a classification accuracy of 93.37%, surpassing that of conventional methods and highlighting the effectiveness of deep learning in biomedical signal processing. This study bridges the gap between machine learning and clinical applications, enabling an interpretable and region-specific classification system that enhances diagnostic precision. Future work will explore cross-domain generalization, multi-modal medical data integration, and real-time deployment for clinical applications. The findings establish a significant advancement in non-invasive diagnostics, demonstrating the potential of deep learning to refine and automate biomedical signal analysis in clinical practice.