Multi-class classification of prostate cancer MRIS based on UCLA score using deep transfer learning models
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Full Text |
Pdf
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Author |
Rajesh M. N. and Chandrasekar B. S.
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e-ISSN |
1819-6608 |
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On Pages
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1997-2008
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Volume No. |
17
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Issue No. |
23
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Issue Date |
15 January 2023 |
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DOI |
https://doi.org/10.59018/122202
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Keywords |
prostate cancer, VGG-16, VGG-19, mobileNet-v3, deep learning, prostate classification, T2w MRI.
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Abstract
Prostate cancer is the most frequent cancer in the men population, and early detection is critical in order to lower
mortality rates from the disease. With its superior soft-tissue contrast, magnetic resonance imaging (MRI) has become the
imaging method of choice for the localization of PCa. In terms of diagnosing PCa of the transition zone, T2-weighted
images are the most useful tool among the several MRI modalities. In this proposed model, the PCa is classified based on
the T2w MRI data. The proposed model is a deep learning approach, which includes the deep transfer learning models for
the classification of PCa. For classifying the data, the different variants of VGG-16, VGG-19, and MobileNet-v3Large
transfer learning models are used. These models are modified using different optimizers for varying the learning rate.
Optimizers like Adam, AdaMax, SGD, RMSprop, and Ftrl are used in this research. For evaluation, the ampMRI dataset
with 845 patient records with unique "UCLA" scores of the ROI was used for multi-class classification. For performance
analysis, accuracy, sensitivity, specificity, precision, and F1 score are computed based on the classification. Finally,
according to the results, the performance was compared among the different proposed models for validation. The proposed
models optimized using the Ftrl optimizer have obtained better performances with 93.31% accuracy, 93.92% accuracy, and
95.27% accuracy for VGG-16-Model-04, VGG-19-Model-04, and MobileNet-v3 respectively.
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