Prashantha HS
Dr. Prasantha H S received Bachelor degreefrom Bangalore University, Master Degree fromV.T.U, Belgaum, and PhD from Anna University,Chennai, in the area of Signal and ImageProcessing. He has 20 years of teaching andresearch experience. His research interest includesMultimedia and Signal Processing. He haspublished more than 35 papers in International conferences andJournals. He is a reviewer for various reputed conferences andJournals. He is currently guiding four students for their researchprogram under VTU. Currently, he is working as a Professor in thedepartment of Electronics and Communication Engineering, NitteMeenakshi Institute of Technology (Affiliated to VTU Belgaum),Bangalore.
Phone: 09902058362
Address: Nagarabhavi
Phone: 09902058362
Address: Nagarabhavi
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preprocessing of the image before it is being deployed on different architectures for
analysis. In recent past, it has been observed that in medical image diagnosis, deep
learning models especially Convolution Networks have been declared as an efficient
technique by most researchers. Since most of the times in biomedical imaging, the choice
of the dataset and the region of interest plays a crucial role in diagnosis of the tumorous
cell in human brain, an attempt has been made here to find the best variant of U-Net deep
learning model for segmentation of the brain image as against the manual and automated
approach of diagnosis of tumor in human brain. This paper investigates the different
variants of the U-Net model before concluding the lighter and the best variant for the
segmentation by taking into consideration fine-tuning of different hyper parameters
involved in decision making. During the experimentation, the best model is selected not
just, because one model outperformed the other in giving better accuracy, instead care
has been ensured in selecting the model that has acceptable higher accuracy performance
while performing fewer computations but giving out faster inference. In this work with
the aid of transfer learning a block wise fine tuning of hyper parameters is carried out to
derive a model with 99.23% of accuracy on BraTs 2019 FLAIR dataset.
preprocessing of the image before it is being deployed on different architectures for
analysis. In recent past, it has been observed that in medical image diagnosis, deep
learning models especially Convolution Networks have been declared as an efficient
technique by most researchers. Since most of the times in biomedical imaging, the choice
of the dataset and the region of interest plays a crucial role in diagnosis of the tumorous
cell in human brain, an attempt has been made here to find the best variant of U-Net deep
learning model for segmentation of the brain image as against the manual and automated
approach of diagnosis of tumor in human brain. This paper investigates the different
variants of the U-Net model before concluding the lighter and the best variant for the
segmentation by taking into consideration fine-tuning of different hyper parameters
involved in decision making. During the experimentation, the best model is selected not
just, because one model outperformed the other in giving better accuracy, instead care
has been ensured in selecting the model that has acceptable higher accuracy performance
while performing fewer computations but giving out faster inference. In this work with
the aid of transfer learning a block wise fine tuning of hyper parameters is carried out to
derive a model with 99.23% of accuracy on BraTs 2019 FLAIR dataset.