Publication Date: 2021/04/17
Abstract: Alom et al. in their article with title Recurrent Residual U-Net for Medical Image Segmentation, published in March 2019 in Journal of Medical Imaging, proposed two deep network architectures for medical image semantic segmentation [1]. These models are evaluated using existing benchmark medical image datasets. This work aims to penetrate the deep learning concept in medicine to minimize human intervention in medical diagnosis. To achieve this goal, the author utilized the power of existing state-of-the-art deep network architectures designed for medical image segmentation, including UNET, residual network, and recurrent convolutional neural network. It is found that deep learning generally anddefined deep neural architectures particularly has an enormous impact to accurately perform medical image analysis.
Keywords: Deep Learning, Critical Review, Image Segmenta- Tion, Convolution Neural Network Sequence.
DOI: No DOI Available
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT21APR172.pdf
REFERENCES