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dc.contributor.authorKumar C.A.
dc.contributor.authorKumar M.T.N.
dc.contributor.authorNarasimhadhan A.V.
dc.identifier.citationProceedings of CONECCT 2020 - 6th IEEE International Conference on Electronics, Computing and Communication Technologies , Vol. , , p. -en_US
dc.description.abstractBiomedical image segmentation is one of the main and fast growing field in medical image processing domain. Deep neural networks is one of the popular field used for image segmentation. Convolutional neural networks(CNNs) in deep neural networks have shown good performance for biomedical image segmentation. However, a strong notion exists that large number of annotated images are required for training of CNNs. Therefore, in this paper we have come up with a modified U-Net architecture for limited number of annotated data with an intersection over union score of 92.54%. The architecture uses rectified-adam optimizer(advanced version of adam) for minimizing the loss function which helps us to come close to global optima. We have also compared the performance of various optimizers on the proposed network. © 2020 IEEE.en_US
dc.titleCell Segmentation by Modified U-Net Architecture for Biomedical Imagesen_US
dc.typeConference Paperen_US
Appears in Collections:2. Conference Papers

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