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Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach.

Tejas Shelatkar1, Dr Urvashi1, Mohammad Shorfuzzaman2

  • 1Department of Computer Science and Engineering, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar 144011, India.

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Summary
This summary is machine-generated.

This study introduces a deep learning model using YOLOv5 for early brain tumor detection from MRI scans. The system achieved 88% precision in identifying malignant tumors like glioblastoma, improving diagnostic capabilities.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Early detection of brain tumors, such as glioblastoma, is critical for patient survival.
  • Magnetic resonance imaging (MRI) is a key technology for visualizing brain structures.
  • Computer-aided diagnosis systems show promise in supporting radiologists.

Purpose of the Study:

  • To develop and evaluate a deep learning approach for detecting and classifying brain tumors using MRI.
  • To investigate the effectiveness of transfer learning with the YOLOv5 object detection framework.

Main Methods:

  • Utilized the YOLOv5 deep learning model for object detection on MRI scans.
  • Employed the Brats 2021 dataset, annotated using AI tools.
  • Applied transfer learning and divided data into training and testing sets.

Main Results:

  • The YOLOv5 model achieved a precision of 88% in detecting brain tumors.
  • The model demonstrated successful detection of malignant tumors across the entire dataset.

Conclusions:

  • Deep learning, specifically YOLOv5, offers a viable approach for automated brain tumor detection from MRI.
  • The proposed method shows potential for enhancing early diagnosis and classification of brain cancers.