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Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data
The automated extraction of critical information from electronic medical records, such as oncological medical events, has become increasingly important with the widespread use of electronic health records. However, extracting tumor-related medical events can be challenging due to their unique charac...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047127/ https://www.ncbi.nlm.nih.gov/pubmed/36980333 http://dx.doi.org/10.3390/diagnostics13061025 |
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author | Cifci, Mehmet Akif Hussain, Sadiq Canatalay, Peren Jerfi |
author_facet | Cifci, Mehmet Akif Hussain, Sadiq Canatalay, Peren Jerfi |
author_sort | Cifci, Mehmet Akif |
collection | PubMed |
description | The automated extraction of critical information from electronic medical records, such as oncological medical events, has become increasingly important with the widespread use of electronic health records. However, extracting tumor-related medical events can be challenging due to their unique characteristics. To address this difficulty, we propose a novel approach that utilizes Generative Adversarial Networks (GANs) for data augmentation and pseudo-data generation algorithms to improve the model’s transfer learning skills for various tumor-related medical events. Our approach involves a two-stage pre-processing and model training process, where the data is cleansed, normalized, and augmented using pseudo-data. We evaluate our approach using the i2b2/UTHealth 2010 dataset and observe promising results in extracting primary tumor site size, tumor size, and metastatic site information. The proposed method has significant implications for healthcare and medical research as it can extract vital information from electronic medical records for oncological medical events. |
format | Online Article Text |
id | pubmed-10047127 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100471272023-03-29 Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data Cifci, Mehmet Akif Hussain, Sadiq Canatalay, Peren Jerfi Diagnostics (Basel) Article The automated extraction of critical information from electronic medical records, such as oncological medical events, has become increasingly important with the widespread use of electronic health records. However, extracting tumor-related medical events can be challenging due to their unique characteristics. To address this difficulty, we propose a novel approach that utilizes Generative Adversarial Networks (GANs) for data augmentation and pseudo-data generation algorithms to improve the model’s transfer learning skills for various tumor-related medical events. Our approach involves a two-stage pre-processing and model training process, where the data is cleansed, normalized, and augmented using pseudo-data. We evaluate our approach using the i2b2/UTHealth 2010 dataset and observe promising results in extracting primary tumor site size, tumor size, and metastatic site information. The proposed method has significant implications for healthcare and medical research as it can extract vital information from electronic medical records for oncological medical events. MDPI 2023-03-08 /pmc/articles/PMC10047127/ /pubmed/36980333 http://dx.doi.org/10.3390/diagnostics13061025 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Cifci, Mehmet Akif Hussain, Sadiq Canatalay, Peren Jerfi Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data |
title | Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data |
title_full | Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data |
title_fullStr | Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data |
title_full_unstemmed | Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data |
title_short | Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data |
title_sort | hybrid deep learning approach for accurate tumor detection in medical imaging data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047127/ https://www.ncbi.nlm.nih.gov/pubmed/36980333 http://dx.doi.org/10.3390/diagnostics13061025 |
work_keys_str_mv | AT cifcimehmetakif hybriddeeplearningapproachforaccuratetumordetectioninmedicalimagingdata AT hussainsadiq hybriddeeplearningapproachforaccuratetumordetectioninmedicalimagingdata AT canatalayperenjerfi hybriddeeplearningapproachforaccuratetumordetectioninmedicalimagingdata |