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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Brain Tumor Segmentation with Deep Learning |
| Authors Name: | KARTHIKA R |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_191611
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| Published Paper Id: | IJNRD2304481 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | 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. |
| Keywords: | SEGMENTATION, DEEP LEARNING, IMAGES, NEURAL NETWORKS. |
| Cite Article: | "Brain Tumor Segmentation with Deep Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 4, page no.e573-e576, April-2023, Available :http://www.ijnrd.org/papers/IJNRD2304481.pdf |
| Downloads: | 000118751 |
| 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 |
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Published Paper ID:IJNRD2304481 Registration ID: 191611 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: e573-e576 Country: Kovilpatti/Thoothukudi, Tamil Nadu, India Research Area: Science Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2304481 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2304481 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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