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Journal of Medical Signals and Sensors، جلد ۱۴، شماره ۱، صفحات ۴-۲۰۲۴
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عنوان انگلیسی Transfer Learning with Pretrained Convolutional Neural Network for Automated Gleason Grading of Prostate Cancer Tissue Microarrays
چکیده انگلیسی مقاله Background:  The Gleason grading system has been the most effective prediction for prostate cancer patients. This grading system provides this possibility to assess prostate cancer’s aggressiveness and then constitutes an important factor for stratification and therapeutic decisions. However, determining Gleason grade requires highly-trained pathologists and is time-consuming and tedious, and suffers from inter-pathologist variability. To remedy these limitations, this paper introduces an automatic methodology based on transfer learning with pretrained convolutional neural networks (CNNs) for automatic Gleason grading of prostate cancer tissue microarray (TMA). Methods:  Fifteen pretrained (CNNs): Efficient Nets (B0-B5), NasNetLarge, NasNetMobile, InceptionV3, ResNet-50, SeResnet 50, Xception, DenseNet121, ResNext50, and inception_resnet_v2 were fine-tuned on a dataset of prostate carcinoma TMA images. Six pathologists separately identified benign and cancerous areas for each prostate TMA image by allocating benign, 3, 4, or 5 Gleason grade for 244 patients. The dataset was labeled by these pathologists and majority vote was applied on pixel-wise annotations to obtain a unified label. Results:  Results showed the NasnetLarge architecture is the best model among them in the classification of prostate TMA images of 244 patients with accuracy of 0.93 and area under the curve of 0.98. Conclusion:  Our study can act as a highly trained pathologist to categorize the prostate cancer stages with more objective and reproducible results.
کلیدواژه‌های انگلیسی مقاله Convolutional neural network,Gleason grading,prostate cancer,transfer learning
نویسندگان مقاله | Parisa Gifani
2Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran 3Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran


| Ahmad shalbaf


نشانی اینترنتی http://jmss.mui.ac.ir/index.php/jmss/article/view/700
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زبان مقاله منتشر شده en
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نوع مقاله منتشر شده Original Articles
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