Comparison of YOLO and transformer based tumor detection in cystoscopy
Background: Bladder cancer (BCa) is the second most common type of cancer in the genitourinary system and causes approximately 165,000 deaths each year. The diagnosis of BCa is primarily done through cystoscopy, which involves visually examining the bladder using an endoscope. Currently, white light...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2024-12-01
|
| Series: | Current Directions in Biomedical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cdbme-2024-2055 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850032100012457984 |
|---|---|
| author | Eixelberger Thomas Maisch Philipp Belle Sebastian Kriegmaier Maximilian Bolenz Christian Wittenberg Thomas |
| author_facet | Eixelberger Thomas Maisch Philipp Belle Sebastian Kriegmaier Maximilian Bolenz Christian Wittenberg Thomas |
| author_sort | Eixelberger Thomas |
| collection | DOAJ |
| description | Background: Bladder cancer (BCa) is the second most common type of cancer in the genitourinary system and causes approximately 165,000 deaths each year. The diagnosis of BCa is primarily done through cystoscopy, which involves visually examining the bladder using an endoscope. Currently, white light cystoscopy is considered the most reliable method for diagnosis. However, it can be challenging to detect and diagnose flat, small, or poorly textured lesions. The study explores the performance of deep learning systems (YOLOv7- tiny, RT-DETR18), originally designed for detecting adenomas in colonoscopy images, when retrained and tested with cystoscopy images. The deep neural network used in the study was pre-trained on 35,699 colonoscopy images (some from Mannheim) and both architectures achieved a F1 score of 0.91 on publicly available colonoscopy datasets. Results: When the adenoma-detection network was tested with cystoscopy images from two sources (Ulm and Erlangen), F1 scores ranging from 0.58 to 0.81 were achieved. Subsequently, the networks were retrained using 12,066 cystoscopy images from Mannheim, resulting in improved F1 scores ranging from 0.77 to 0.85. Conclusion: It could be shown that transformer based networks perform slightly better than YOLOv7-tiny networks, but both network types are feasable for lesion detection in the human bladder. The retraining of the network with additional cystoscopy data led to an improvement in the performance of urinary lesion detection. This suggests that it is possible to achieve a domain-shift with the inclusion of appropriate additional data. |
| format | Article |
| id | doaj-art-c8f8cbd98a744eb1a238e72b76fbb0a7 |
| institution | DOAJ |
| issn | 2364-5504 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj-art-c8f8cbd98a744eb1a238e72b76fbb0a72025-08-20T02:58:46ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042024-12-0110422823110.1515/cdbme-2024-2055Comparison of YOLO and transformer based tumor detection in cystoscopyEixelberger Thomas0Maisch Philipp1Belle Sebastian2Kriegmaier Maximilian3Bolenz Christian4Wittenberg Thomas5Friedrich-Alexander-Universitat Erlangen-Nurnberg & Fraunhofer IIS,Erlangen, GermanyDepartment of Urology, University Hospital Ulm,Ulm, GermanyUniversitats-Medizin,Mannheim, GermanyUrology Clinics Munchen-Planegg,Munchen, GermanyDepartment of Urology, University Hospital Ulm,Ulm, GermanyFriedrich-Alexander-Universitat Erlangen-Nurnberg & Fraunhofer IIS,Erlangen, GermanyBackground: Bladder cancer (BCa) is the second most common type of cancer in the genitourinary system and causes approximately 165,000 deaths each year. The diagnosis of BCa is primarily done through cystoscopy, which involves visually examining the bladder using an endoscope. Currently, white light cystoscopy is considered the most reliable method for diagnosis. However, it can be challenging to detect and diagnose flat, small, or poorly textured lesions. The study explores the performance of deep learning systems (YOLOv7- tiny, RT-DETR18), originally designed for detecting adenomas in colonoscopy images, when retrained and tested with cystoscopy images. The deep neural network used in the study was pre-trained on 35,699 colonoscopy images (some from Mannheim) and both architectures achieved a F1 score of 0.91 on publicly available colonoscopy datasets. Results: When the adenoma-detection network was tested with cystoscopy images from two sources (Ulm and Erlangen), F1 scores ranging from 0.58 to 0.81 were achieved. Subsequently, the networks were retrained using 12,066 cystoscopy images from Mannheim, resulting in improved F1 scores ranging from 0.77 to 0.85. Conclusion: It could be shown that transformer based networks perform slightly better than YOLOv7-tiny networks, but both network types are feasable for lesion detection in the human bladder. The retraining of the network with additional cystoscopy data led to an improvement in the performance of urinary lesion detection. This suggests that it is possible to achieve a domain-shift with the inclusion of appropriate additional data.https://doi.org/10.1515/cdbme-2024-2055deep learningyolotransformerscystoscopylesion detection |
| spellingShingle | Eixelberger Thomas Maisch Philipp Belle Sebastian Kriegmaier Maximilian Bolenz Christian Wittenberg Thomas Comparison of YOLO and transformer based tumor detection in cystoscopy Current Directions in Biomedical Engineering deep learning yolo transformers cystoscopy lesion detection |
| title | Comparison of YOLO and transformer based tumor detection in cystoscopy |
| title_full | Comparison of YOLO and transformer based tumor detection in cystoscopy |
| title_fullStr | Comparison of YOLO and transformer based tumor detection in cystoscopy |
| title_full_unstemmed | Comparison of YOLO and transformer based tumor detection in cystoscopy |
| title_short | Comparison of YOLO and transformer based tumor detection in cystoscopy |
| title_sort | comparison of yolo and transformer based tumor detection in cystoscopy |
| topic | deep learning yolo transformers cystoscopy lesion detection |
| url | https://doi.org/10.1515/cdbme-2024-2055 |
| work_keys_str_mv | AT eixelbergerthomas comparisonofyoloandtransformerbasedtumordetectionincystoscopy AT maischphilipp comparisonofyoloandtransformerbasedtumordetectionincystoscopy AT bellesebastian comparisonofyoloandtransformerbasedtumordetectionincystoscopy AT kriegmaiermaximilian comparisonofyoloandtransformerbasedtumordetectionincystoscopy AT bolenzchristian comparisonofyoloandtransformerbasedtumordetectionincystoscopy AT wittenbergthomas comparisonofyoloandtransformerbasedtumordetectionincystoscopy |