Distinguishing Bladder Cancer from Cystitis Patients Using Deep Learning
Urinary tract cancers are considered life-threatening conditions worldwide, and Bladder Cancer is one of the most malignant urinary tract tumors, with an estimated number of more than 1.3 million cases worldwide each year. Bladder Cancer is a heterogeneous disease; the main symptom is painless hemat...
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MDPI AG
2023-09-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/19/4118 |
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| author | Dong-Her Shih Pai-Ling Shih Ting-Wei Wu Chen-Xuan Lee Ming-Hung Shih |
| author_facet | Dong-Her Shih Pai-Ling Shih Ting-Wei Wu Chen-Xuan Lee Ming-Hung Shih |
| author_sort | Dong-Her Shih |
| collection | DOAJ |
| description | Urinary tract cancers are considered life-threatening conditions worldwide, and Bladder Cancer is one of the most malignant urinary tract tumors, with an estimated number of more than 1.3 million cases worldwide each year. Bladder Cancer is a heterogeneous disease; the main symptom is painless hematuria. However, patients with Bladder Cancer may initially be misdiagnosed as Cystitis or infection, and cystoscopy alone may sometimes be misdiagnosed as urolithiasis or Cystitis, thereby delaying medical attention. Early diagnosis of Bladder Cancer is the key to successful treatment. This study uses six deep learning methods through different oversampling techniques and feature selection, and then through dimensionality reduction techniques, to establish a set that can effectively distinguish between Bladder Cancer and Cystitis patient’s deep learning model. The research results show that based on the laboratory clinical dataset, the deep learning model proposed in this study has an accuracy rate of 89.03% in distinguishing between Bladder Cancer and Cystitis, surpassing the results of previous studies. The research model developed in this study can be provided to clinicians as a reference to differentiate between Bladder Cancer and Cystitis. |
| first_indexed | 2024-03-10T21:40:02Z |
| format | Article |
| id | doaj.art-a1622c6d7b394c208f481f3448b1da19 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T21:40:02Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-a1622c6d7b394c208f481f3448b1da192023-11-19T14:43:36ZengMDPI AGMathematics2227-73902023-09-011119411810.3390/math11194118Distinguishing Bladder Cancer from Cystitis Patients Using Deep LearningDong-Her Shih0Pai-Ling Shih1Ting-Wei Wu2Chen-Xuan Lee3Ming-Hung Shih4Department of Information Management, National Yunlin University of Science and Technology, Douliu 64002, TaiwanDepartment of Information Management, National Chung Cheng University, Chiayi 621301, TaiwanDepartment of Information Management, National Yunlin University of Science and Technology, Douliu 64002, TaiwanDepartment of Information Management, National Yunlin University of Science and Technology, Douliu 64002, TaiwanDepartment of Electrical and Computer Engineering, Iowa State University, 2520 Osborn Drive, Ames, IA 50011, USAUrinary tract cancers are considered life-threatening conditions worldwide, and Bladder Cancer is one of the most malignant urinary tract tumors, with an estimated number of more than 1.3 million cases worldwide each year. Bladder Cancer is a heterogeneous disease; the main symptom is painless hematuria. However, patients with Bladder Cancer may initially be misdiagnosed as Cystitis or infection, and cystoscopy alone may sometimes be misdiagnosed as urolithiasis or Cystitis, thereby delaying medical attention. Early diagnosis of Bladder Cancer is the key to successful treatment. This study uses six deep learning methods through different oversampling techniques and feature selection, and then through dimensionality reduction techniques, to establish a set that can effectively distinguish between Bladder Cancer and Cystitis patient’s deep learning model. The research results show that based on the laboratory clinical dataset, the deep learning model proposed in this study has an accuracy rate of 89.03% in distinguishing between Bladder Cancer and Cystitis, surpassing the results of previous studies. The research model developed in this study can be provided to clinicians as a reference to differentiate between Bladder Cancer and Cystitis.https://www.mdpi.com/2227-7390/11/19/4118CystitisBladder Cancerdeep learningdimensionality reductiondata imbalance |
| spellingShingle | Dong-Her Shih Pai-Ling Shih Ting-Wei Wu Chen-Xuan Lee Ming-Hung Shih Distinguishing Bladder Cancer from Cystitis Patients Using Deep Learning Mathematics Cystitis Bladder Cancer deep learning dimensionality reduction data imbalance |
| title | Distinguishing Bladder Cancer from Cystitis Patients Using Deep Learning |
| title_full | Distinguishing Bladder Cancer from Cystitis Patients Using Deep Learning |
| title_fullStr | Distinguishing Bladder Cancer from Cystitis Patients Using Deep Learning |
| title_full_unstemmed | Distinguishing Bladder Cancer from Cystitis Patients Using Deep Learning |
| title_short | Distinguishing Bladder Cancer from Cystitis Patients Using Deep Learning |
| title_sort | distinguishing bladder cancer from cystitis patients using deep learning |
| topic | Cystitis Bladder Cancer deep learning dimensionality reduction data imbalance |
| url | https://www.mdpi.com/2227-7390/11/19/4118 |
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