Brain Tumor Detection Using Deep Learning .
Katta Sudheer Reddy
, Piridi Akshay , Meruva Supriya Reddy , Dhanni Pavani , Bhaskar Das
Brain Tumor, Deep learning, MRI scans, Xception model , Mobilenet , CNN model , Global max pooling, softmax pooling
Brain tumor detection from MRI scans plays a crucial role in early diagnosis and treatment planning. Existing solutions rely on traditional image processing techniques, often limited by their inability to handle complex patterns and variations in medical images. Moreover, these methods may lack the scalability and adaptability required for real-world clinical applications. To address these limitations, this study proposes a deep learning-based approach for brain tumor detection. Three prominent architectures, Convolutional Neural Networks (CNN), MobileNet, and Xception are evaluated on a dataset comprising 7770 MRI images. Preprocessing techniques including normalization, resizing, augmentation, and segmentation are employed to enhance data quality and model performance. The models are trained using the Adam optimizer with categorical cross-entropy loss. Evaluation results reveal Xception as the most effective model, achieving an accuracy of 98%. Future work will focus on further optimizing model parameters, exploring advanced data augmentation methods, and integrating clinical data to enhance diagnostic accuracy and model generalization. This study contributes to advancing the field of medical image analysis, with implications for improving patient outcomes in neuroimaging.
"Brain Tumor Detection Using Deep Learning .", JNRID - JOURNAL OF NOVEL RESEARCH AND INNOVATIVE DEVELOPMENT (www.JNRID.org), ISSN:2984-8687, Vol.2, Issue 5, page no.a269-a275, May-2024, Available :https://tijer.org/JNRID/papers/JNRID2405032.pdf
Volume 2
Issue 5,
May-2024
Pages : a269-a275
Paper Reg. ID: JNRID_700348
Published Paper Id: JNRID2405032
Downloads: 000506
Research Area: Science and Technology
Country: KUKATPALLY,HYDERABAD, Telangana, India
ISSN: 2984-8687 | IMPACT FACTOR: 9.57 Calculated By Google Scholar | ESTD YEAR: 2023
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: JNRID (IJ Publication) Janvi Wave
