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Automated Gleason grading of prostate cancer using transfer learning from general-purpose deep-learning networks
- Source :
- Romanian Journal of Morphology and Embryology
- Publication Year :
- 2020
- Publisher :
- Academy of Medical Sciences, Romanian Academy Publishing House, Bucharest, 2020.
-
Abstract
- Two deep-learning algorithms designed to classify images according to the Gleason grading system that used transfer learning from two well-known general-purpose image classification networks (AlexNet and GoogleNet) were trained on Hematoxylin-Eosin histopathology stained microscopy images with prostate cancer. The dataset consisted of 439 images asymmetrically distributed in four Gleason grading groups. Mean and standard deviation accuracy for AlexNet derivate network was of 61.17±7 and for GoogleNet derivate network was of 60.9±7.4. The similar results obtained by the two networks with very different architecture, together with the normal distribution of classification error for both algorithms show that we have reached a maximum classification rate on this dataset. Taking into consideration all the constraints, we conclude that the resulted networks could assist pathologists in this field, providing first or second opinions on Gleason grading, thus presenting an objective opinion in a grading system which has showed in time a great deal of interobserver variability.
- Subjects :
- Male
Embryology
Computer science
030232 urology & nephrology
Gleason grading
transfer learning
Standard deviation
Pathology and Forensic Medicine
AlexNet
03 medical and health sciences
Prostate cancer
0302 clinical medicine
Deep Learning
GoogleNet
Gleason grading system
medicine
Humans
Original Paper
Contextual image classification
business.industry
Deep learning
Prostatic Neoplasms
Pattern recognition
Cell Biology
General Medicine
medicine.disease
prostate cancer
General purpose
030220 oncology & carcinogenesis
Artificial intelligence
Neoplasm Grading
Transfer of learning
business
Algorithms
Developmental Biology
Subjects
Details
- Language :
- English
- ISSN :
- 20668279 and 12200522
- Volume :
- 61
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Romanian Journal of Morphology and Embryology
- Accession number :
- edsair.doi.dedup.....d11ed4c48053b0f1e80f9ba3d9f2582c