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IJNRD
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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Impact Factor : 8.76

Issue per Year : 12

Volume Published : 7

Issue Published : 78

Article Submitted : 4554

Article Published : 2142

Total Authors : 5243

Total Reviewer : 661

Total Countries : 73

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Published Paper Details
Paper Title: Identification and Detection of Intracranial Hemorrhage Using Deep Learning
Authors Name: L.M.Varsha , Sudha K.L , Padmini Prabhakar
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IJNRD_183025
Published Paper Id: IJNRD2209146
Published In: Volume 7 Issue 9, September-2022
DOI:
Abstract: Intracranial hemorrhage (ICH) is a serious medical emergency that requires quick and accurate assessment and treatment. Mortality rate due to brain hemorrhage is very high (approximately 40%) as per reports. Hence early detection and classification on non-contrast computed tomography (CT) is essential for a proper prediction and limiting the occurrence of neurologic problems. However, in present scenario, there is a delay in the early detection of ICHs due to a lack of number of radiologists who can read the scans. Therefore, an automatic notification system using the deep-learning artificial intelligence (AI) method has been introduced for the detection of ICH. Recently, deep-learning methods are tried for the detection of ICH on CT images. Deep-learning methods are ML algorithms that use multiple processing layers to learn representations of data with multiple levels of abstraction. This work builds a convolutional neural network based on ResNet for the identification and classification of ICH. Using dataset collected from four international universities by the Radiological Society of North America (RSNA), training and testing a ResNet-50 based CNN model is done for predicting the hemorrhage and its type. Analysis shows that accuracy of up to 94% can be achieved in identifying the correct type of ICH.
Keywords: Intracranial Hemorrhage, CT scans, Deep learning, CNN, ResNet-50
Cite Article: "Identification and Detection of Intracranial Hemorrhage Using Deep Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 9, page no.1254-1257, September-2022, Available :http://www.ijnrd.org/papers/IJNRD2209146.pdf
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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
Publication Details: Published Paper ID:IJNRD2209146
Registration ID: 183025
Published In: Volume 7 Issue 9, September-2022
DOI (Digital Object Identifier):
Page No: 1254-1257
Country: Bengaluru, Karnataka, India
Research Area: Engineering
Publisher : IJ Publication
Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2209146
Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2209146
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016

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