Evaluasi Model Deep Learning pada Pola Dataset Biomedis

Authors

  • Gunawan Gunawan STMIK YMI Tegal
  • Septian Ari Wibowo STMIK YMI Tegal
  • Wresti Andriani STMIK YMI Tegal

DOI:

https://doi.org/10.33020/saintekom.v14i2.738

Keywords:

Biomedical Data, CNN (Convolutional Neural Networks), Deep Learning, Medical Image Analysis, RNN (Recurrent Neural Networks)

Abstract

This study aims to evaluate the effectiveness and efficiency of various deep learning models in recognizing patterns within diverse biomedical datasets. The methods involved the collection of biomedical data from various public and synthetic sources, including chest radiographs, MRI, CT scans, as well as electrocardiogram (ECG) and electromyography (EMG) signals. The data underwent preprocessing steps such as normalization, noise removal, and data augmentation to improve quality and variability. The deep learning models evaluated included Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which were trained to identify patterns within the data. The performance evaluation was conducted using metrics like accuracy, sensitivity, specificity, and AUC to ensure the models' generalization capabilities on test datasets. The results revealed that CNNs excelled in medical image analysis, particularly in terms of accuracy and interpretability, while RNNs were more effective in handling sequential data such as medical signals. The primary conclusion of this study is that the selection of deep learning models should be tailored to the type of data and specific application requirements, emphasizing the importance of improving model interpretability and generalization for broader applications in clinical settings.

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Published

30-09-2024

How to Cite

Gunawan, Gunawan, Septian Ari Wibowo, and Wresti Andriani. 2024. “Evaluasi Model Deep Learning Pada Pola Dataset Biomedis”. Jurnal Saintekom : Sains, Teknologi, Komputer Dan Manajemen 14 (2):195-207. https://doi.org/10.33020/saintekom.v14i2.738.

Issue

Vol. 14 No. 2 (2024): September 2024

Section

Articles

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Copyright (c) 2024 Gunawan Gunawan, Septian Ari Wibowo, Wresti Andriani

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