Kidney Segmentations Using CNN models

Authors : Mohammed MANSOUR, Mert Süleyman DEMİRSOY, Mustafa Çağrı KUTLU

Pages : 1-13

Doi:10.58769/joinssr.1175622

View : 45 | Download : 29

Publication Date : 2023-06-24

Article Type : Research Paper

Abstract :For medical diagnostic tests, kidney segmentation from high-volume imagery is an important major. Since 3D medical images need a lot of GPU memory, slices and patches are used for training and inference in traditional neural network variant architectures, which necessarily slows down contextual learning. In this research, Mobile Net and Efficient Net CNN models were trained for segmenting human kidney images generated from The Human Biomolecular Atlas Program insert ignore into journalissuearticles values(HuBMAP);. The purpose of this work is to evaluate the effectiveness of different strategies for Glomeruli identification in order to solve the issue. The high size images were decoded to be fitted and trained in the models first, then the CNN models were trained. The CNN models result show that the Efficient Net has the highest accuracy rate with 99.49 %, and Mobile Net with 99.33 %.
Keywords : kideny, segmeantion, CNN