Automatic Segmentation of Prostate Cancer using cascaded Fully Convolutional Network

In this paper we proposed a prostate segmentation and also tumour detection using deep neural networks. The cutting-edge deep learning techniques are useful compared to the challenges of machine learning based feature extraction techniques. Here we proposed a strategy that contains an FCN model that...

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Bibliographic Details
Published inE3S Web of Conferences Vol. 309; p. 1068
Main Authors Kora, Padmavathi, Reddy Madhavi, K, Avanija, J, Gurram, Sunitha, Meenakshi, K, Swaraja, K, Priyanka, Y
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 01.01.2021
Subjects
Artificial neural networks
cnn
Deep learning
fcn
Feature extraction
Image segmentation
Learning algorithms
Machine learning
Magnetic resonance imaging
mr images
Neural networks
Prostate cancer
Tumors
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Summary:In this paper we proposed a prostate segmentation and also tumour detection using deep neural networks. The cutting-edge deep learning techniques are useful compared to the challenges of machine learning based feature extraction techniques. Here we proposed a strategy that contains an FCN model that incorporates data from several MRI images, allowing for faster convergence and more accurate segmentation. T1 and DWI volumes may be used together to delineate the prostate boundary, according to this study. Second, we investigated whether this method might be utilized to provide voxel-level prostate tumor forecasts. The cascaded learning method and performed tests to demonstrate its effectiveness.
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202130901068