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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21470
Article Published : 8508
Total Authors : 22383
Total Reviewer : 805
Total Countries : 157
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Published Paper Details
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| Paper Title: | Deciphering Brain Tumor Using Deep Learning For Accurate Classification And Detection |
| Authors Name: | N. Naga Raju , M.Sai Chaitya , K. Manohar , S. Mahesh , M. Naga Sai Sri Lakshmi |
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| Author Reg. ID: |
IJRTI_189727
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| Published Paper Id: | IJRTI2404138 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | Brain tumor detection and classification play a crucial role in early diagnosis and treatment planning for patients with neurological disorders. In recent years, deep learning techniques have shown promising results in automating the analysis of medical images, particularly magnetic resonance imaging (MRI) scans of the brain. This project proposes a comprehensive solution for brain tumor detection and classification using deep learning algorithms. The proposed system comprises several key components, including data preprocessing, feature extraction, deep learning model development, and performance evaluation. MRI images of the brain are preprocessed to enhance their quality and standardize their format. Next, features are extracted from the preprocessed images using techniques such as principal component analysis (PCA), texture analysis, and convolutional neural networks (CNNs).Deep learning models, primarily CNN architectures, are trained on labeled MRI datasets to learn representations of the extracted features and classify brain tumors into different categories, such as glioma, meningioma, pituitary adenoma, and non-tumor. Training is performed using optimization algorithms like stochastic gradient descent (SGD) to minimize classification loss and improve model performance. The trained models are evaluated using various metrics, including accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC), to assess their effectiveness in tumor detection and classification. The proposed system integrates the trained models into a user-friendly software interface for seamless deployment and usage in clinical environments. |
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| Cite Article: | "Deciphering Brain Tumor Using Deep Learning For Accurate Classification And Detection", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 4, page no.1022 - 1026, April-2024, Available :http://www.ijrti.org/papers/IJRTI2404138.pdf |
| Downloads: | 000205386 |
| 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: IJRTI2404138
Registration ID:189727
Published In: Volume 9 Issue 4, April-2024
DOI (Digital Object Identifier):
Page No: 1022 - 1026 Country: Mopidevi, ANDHRA PRADESH, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2404138 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2404138 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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