Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Cifci, Mehmet Akif, Hussain, Sadiq, Canatalay, Peren Jerfi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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
_version_ 1785013843875856384
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