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

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 regio...

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Bibliographic Details
Main Authors: Abdul Karim, Semin Ryu, In cheol Jeong
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Applied Sciences
Subjects:
biomedical signal processing
spectrogram-based feature extraction
deep learning
ResNet50-based classification
anatomical region classification
non-invasive medical diagnostics
Online Access:https://www.mdpi.com/2076-3417/15/10/5313
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