CNN and Transfer Learning Techniques for Improved Brain Tumor Classification from MRI

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Umbare Rupali Tukaram

CNN and Transfer Learning Techniques for Improved Brain Tumor Classification from MRI

Authors: Umbare Rupali Tukaram

Unique Paper ID: 183471
Volume: 12
Issue: 3
PageNo: 1659-1665

Keywords: Brain tumor BraTS Convolutional Neural Networks (CNN) Deep learning

Abstract:
Brain tumors are among the most dangerous types of cancer and highlight the importance of a timely and accurate diagnosis. An example of a non-invasive, 1-source modality to identify and assess brain tumors is Magnetic Resonance Imaging (MRI). Interpreting MRI data manually via visual inspection is time-consuming and can be error-prone. To that end, this study proposes an automated classification framework with deep learning models such as Convolutional Neural Networks (CNN), and hybrid approaches of CNN and Support Vector Machine (CNN+SVM) and CNN and the k-nearest neighbors algorithm (CNN+KNN). Transfer learning is also used by using fine-tuned pre-trained networks of InceptionV3 and Xception. The experiments were performed on both the Sartaj dataset and the BraTS dataset. The results indicate that transfer learning can aid my classification performance, with Xception achieving a maximum accuracy of 94.1%. Furthermore, the proposed approach demonstrates statistically reliable and robust results.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: 3
PageNo: 1659-1665

CNN and Transfer Learning Techniques for Improved Brain Tumor Classification from MRI

Available:https://ijirt.org/article?manuscript=183471

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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