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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/6/1025 |
| _version_ | 1797612448678674432 |
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| author | Mehmet Akif Cifci Sadiq Hussain Peren Jerfi Canatalay |
| author_facet | Mehmet Akif Cifci Sadiq Hussain Peren Jerfi Canatalay |
| author_sort | Mehmet Akif Cifci |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T06:42:22Z |
| format | Article |
| id | doaj.art-fccd71555fac483a921691b7b355ba21 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T06:42:22Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-fccd71555fac483a921691b7b355ba212023-11-17T10:33:18ZengMDPI AGDiagnostics2075-44182023-03-01136102510.3390/diagnostics13061025Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging DataMehmet Akif Cifci0Sadiq Hussain1Peren Jerfi Canatalay2The Institute of Computer Technology, Tu Wien University, 1040 Vienna, AustriaExamination Branch, Dibrugarh University, Dibrugarh 786004, Assam, IndiaDepartment of Computer Engineering, Haliç University, Istanbul 34394, TurkeyThe 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.https://www.mdpi.com/2075-4418/13/6/1025electronic medical recordsmedical event extractiontransfer learningjoint extraction |
| spellingShingle | Mehmet Akif Cifci Sadiq Hussain Peren Jerfi Canatalay Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data Diagnostics electronic medical records medical event extraction transfer learning joint extraction |
| 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 | electronic medical records medical event extraction transfer learning joint extraction |
| url | https://www.mdpi.com/2075-4418/13/6/1025 |
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