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Impact Factor : 8.14

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Paper Title: Design of Computer Aided Diagnosis System for Effective Automatic Segmentation of Brain MRI Images
Authors Name: Dr. Manoranjan Dash , Taduka Sai Tehith , Bommana Sathwik Rao , Bhuwan Goud Badagouni
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IJRTI_189406
Published Paper Id: IJRTI2403071
Published In: Volume 9 Issue 3, March-2024
DOI:
Abstract: The field of medical imaging has witnessed significant advancements in recent years, with the integration of computer-aided diagnosis (CAD) systems playing a pivotal role in enhancing the accuracy and efficiency of diagnostic processes. This project focuses on the development of a CAD system specifically tailored for the automatic segmentation of brain MRI images, employing the powerful U-Net architecture. Medical imaging, especially magnetic resonance imaging (MRI), serves as a cornerstone in the diagnosis and monitoring of neurological disorders. Accurate and precise segmentation of brain structures from MRI images is crucial for clinical assessments, treatment planning, and research purposes. Manual segmentation, traditionally performed by radiologists, is time-consuming, subjective, and prone to inter-observer variability. The proposed CAD system aims to address these challenges by automating the segmentation process through the implementation of the U-Net neural network architecture. The U-Net architecture, characterized by a U-shaped network structure, has demonstrated remarkable success in various medical image segmentation tasks. Its unique design features an encoder path for capturing contextual information and a decoder path for achieving high-resolution segmentation maps. The system leverages the deep learning capabilities of U-Net to learn intricate patterns and spatial relationships within brain MRI images, enabling robust and accurate segmentation results. To evaluate the performance of the proposed CAD system, a comprehensive set of metrics will be employed, including but not limited to dice similarity coefficient, sensitivity, specificity, and Hausdorff distance. These metrics will provide quantitative insights into the accuracy and reliability of the automated segmentation results compared to manual annotations. By harnessing the power of deep learning and U-Net architecture, the proposed CAD system aims to enhance the accuracy, efficiency, and objectivity of brain MRI segmentation, ultimately contributing to improved patient care and outcomes in the field of neuroimaging.
Keywords: Brain Tumor Segmentation, Convolutional Neural Network, U-Net Architecture
Cite Article: "Design of Computer Aided Diagnosis System for Effective Automatic Segmentation of Brain MRI Images", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.9, Issue 3, page no.493 - 503, March-2024, Available :http://www.ijrti.org/papers/IJRTI2403071.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2403071
Registration ID:189406
Published In: Volume 9 Issue 3, March-2024
DOI (Digital Object Identifier):
Page No: 493 - 503
Country: Rajanna Sircilla, Telangana, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2403071
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2403071
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

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