DR-UNET: A Hybrid Model For Classification of G lioma using Transfer Learning on MR Images

DR-UNET: A Hybrid Model For Classification of G lioma using Transfer Learning on MR Images

© 2021 by IJETT Journal
Volume-69 Issue-10
Year of Publication : 2021
Authors : Dheeraj D, Prasantha H S
DOI :  10.14445/22315381/IJETT-V69I10P218

How to Cite?

Dheeraj D, Prasantha H S, "DR-UNET: A Hybrid Model For Classification of G lioma using Transfer Learning on MR Images," International Journal of Engineering Trends and Technology, vol. 69, no. 10, pp. 146-150, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I10P218

In medical imaging, one of the tough tasks is the classification of tumors present in the human brain. The work concentrates on the detection of the exact infected location in the human brain that consists of tumor and provide suitable techniques to administer treatment for the same. To achieve this objective, although there are various deep learning techniques employed by different researchers, an attempt has been made in this work to diagnose the existence of tumors using transfer learning. The experimentation has been carried out on the standard benchmark dataset-BraTs 2018. A hybrid model has been developed in this work for classifying the tumor as benign or malignant. The hybrid model is developed using depth-wise convolutions instead of the traditional approach in combination with the residual block being introduced in the final layer into the modified U-Net model deployed using a pre-trained VGG-16 model for classification. This hybrid model was then fine-tuned by varying certain vital hyperparameters to obtain an accuracy of about 92.30%.

Transfer Learning, Deptwise-CNN, VGG-16, Semantic Segmentation, Residual U-Net, Fine Tuning.

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