Brain Tumor Segmentation with Deep Learning

Brain Tumor Segmentation with Deep Learning

Registration ID: IJNRD_191611

Published ID: IJNRD2304481

DOI: Click Here to Get

KARTHIKA R

SEGMENTATION, DEEP LEARNING, IMAGES, NEURAL NETWORKS.

Background Detection and segmentation of brain tumors using MR images are challenging and valuable tasks in the medical field. Early diagnosing and localizing of brain tumors can save lives and provide timely options for physicians to select efficient treatment plans. Deep learning approaches have attracted researchers in medical imaging due to their capacity, performance, and potential to assist in accurate diagnosis, prognosis, and medical treatment technologies. Methods and procedures: This paper presents a novel framework for segmenting 2D brain tumors in MR images using deep neural networks (DNN) and utilizing data augmentation strategies. The proposed approach (Znet) is based on the idea of skip-connection, encoder-decoder architectures, and data amplification to propagate the intrinsic affinities of a relatively smaller number of expert-delineated tumors, e.g., hundreds of patients of the low-grade glioma (LGG), too many thousands of synthetic cases. Results: Our experimental results showed high values of the mean dice similarity coefficient (dice = 0.96 during model training and dice = 0.92 for the independent testing dataset). Other evaluation measures were also relatively high, e.g., pixel accuracy = 0.996, F1 score = 0.81, and Matthews Correlation Coefficient, MCC = 0.81. The results and visualization of the DNN-derived tumor masks in the testing dataset showcase the ZNet model’s capability to localize and auto-segment brain tumors in MR images. This approach can further be generalized to 3D brain volumes, other pathologies, and a wide range of image modalities.

Published Paper   E-Certificate

"Brain Tumor Segmentation with Deep Learning", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 4, page no.e573-e576, April-2023, Available :https://ijnrd.org/papers/IJNRD2304481.pdf

Volume 8 Issue 4, April-2023

Pages : e573-e576

Paper Reg. ID: IJNRD_191611

Published Paper Id: IJNRD2304481

Downloads: 000121107

Research Area: Science

Country: Kovilpatti/Thoothukudi, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304481.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304481

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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