Seminar Title
Performance Study of Optimizers for Segmentation of Brain Tumors using Atrous Convolution in U-Net
Seminar Type
Departmental Seminar
Department
Electronics and Communication Engineering
Speaker Name
Pranshu Jena
Speaker Type
Student
Venue
EC303, Seminar Room
Date & Time
30 May 2023 04.30PM
Contact
Prof U C Pati
Abstract
The purpose of segmentation of the tumourous region in the brain is to identify the tumorous brain
tissues and their location. Manually segmenting the brain tumor is not only time-consuming and error-prone
but also increases the rate of mortality. In the time of need for quick and precise segmentation, many
convolution neural networks have shown exceptionally good execution. In medical image segmentation, the
most favoured network that has been used is U-Net. In this work, reinstatement of the 2D convolution layer
with a 2D dilated convolution layer or atrous convolution layer with a dilation rate as 2 has been
implemented. The performance has been analyzed during the training and testing of the model. Different
optimizers like Stochastic Gradient Descent (SGD), SGD along with momentum, Adam, and Nadam
(Nesterov Adam) have been used in the U-Net model. MICCAI BraTS 2020 dataset has been used in this
work. From the simulated results, it has been observed that the Nadam optimizer along with the atrous
convolution layer provides higher accuracy.