Document Type : Primary Research paper
Bannari Amman Institute of Technology, Erode, Tamilnadu, India
VIT Bhopal University, Madhya Pradesh, India
PSG College of Technology, Coimbatore, Tamilnadu, India
Excel College of Engineering and Technology, Namakkal, Tamilnadu, India
Segmentation of brain Gliomas from MRI autonomously is one of the important
tasks for accurate diagnosis and efficient treatment procedures. Lot of recent researches
involves many deep learning models for predicting the results in a proficient manner.
Some researches include Convolutional Neural Networks, both 2D and 3D approach. But
results gained through CNN are not promising and it is time and memory consuming. In
our proposed work, a com- puter vision based package FastAI based Dynamic_UNet model
is utilized. The model is fine_tuned by comparing the results with Classical_UNet. The
model results are visualized and the optimized parameters are chosen for segmentation
process using Neptune AI logger tool. The accuracy obtained is nearly close to the ground
truth results. The loss obtained is less than 0.005% with an accuracy of more than 87%.
The model results helps to overcome the uncertainty infor- mation obtained due to falsesegmentations
and helps to perk up the prediction accuracy.