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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Brain Hemorrhage Detection Using CNN |
| Authors Name: | Omshree , Sanjana , Ambika , Dr. Suvarna Nandyal |
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| Author Reg. ID: |
IJRTI_204451
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| Published Paper Id: | IJRTI2506066 |
| Published In: | Volume 10 Issue 6, June-2025 |
| DOI: | |
| Abstract: | Brain hemorrhage is a critical and life-threatening condition that requires prompt diagnosis and treatment. Traditionally, detection is performed manually by radiologists through analysis of CT or MRI scans, which can be time-consuming and prone to human error. With advancements in artificial intelligence, particularly deep learning, automated systems have been developed to assist in the early and accurate detection of hemorrhages. This research focuses on the application of Convolutional Neural Networks (CNNs) for the detection and classification of brain hemorrhages from medical imaging data. CNNs, known for their exceptional performance in image recognition tasks, are employed to learn hierarchical features directly from CT scan images without the need for manual feature extraction. The proposed model is trained on a labelled dataset of brain scans, incorporating various types of hemorrhages such as epidural, subdural, subarachnoid, and intraparenchymal. The network architecture is designed to optimize both sensitivity and specificity, thereby ensuring accurate identification of hemorrhagic regions. The system works by preprocessing the input images, feeding them into the CNN for feature extraction and classification, and finally outputting a diagnostic prediction indicating the presence and type of hemorrhage. The model's performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. Results demonstrate that the CNN-based approach achieves high diagnostic accuracy, offering a reliable and scalable solution for clinical use. In conclusion, the integration of CNNs in brain hemorrhage detection can significantly enhance diagnostic workflows, reduce error rates, and ultimately improve patient outcomes by facilitating timely medical intervention. |
| Keywords: | Brain hemorrhage, CNN (Convolutional Neural Network), Neuroimaging, CT scan images, medical image analysis, automatic detection, AI in healthcare, image classification, computer-aided diagnosis |
| Cite Article: | "Brain Hemorrhage Detection Using CNN", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.a571-a584, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506066.pdf |
| Downloads: | 000479 |
| 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 |
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Published Paper ID: IJRTI2506066
Registration ID:204451
Published In: Volume 10 Issue 6, June-2025
DOI (Digital Object Identifier):
Page No: a571-a584 Country: Kalburgi, Karnataka, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2506066 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2506066 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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