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PROJECT IMPLEMENTATION ON : BRAIN TUMOR DETECTION USING IMAGE PROCESSING

  • Unique Paper ID: 175921
  • Volume: 11
  • Issue: 11
  • PageNo: 4636-4647
  • Abstract:
  • Brain tumors are one of the leading causes of death worldwide, and their early detection plays a crucial role in improving patient survival rates. Traditional diagnostic methods, such as MRI and CT scans, require expert interpretation by radiologists, which can be time-consuming and prone to human error. To address these challenges, this project proposes an automated Brain Tumor Detection System that utilizes computer vision and deep learning techniques for accurate and efficient tumor identification from brain imaging data. The system integrates the YOLO (You Only Look Once) deep learning model, a state-of-the-art object detection algorithm, for real-time tumor detection in medical images. It applies various image processing techniques such as denoising, CLAHE (Contrast Limited Adaptive Histogram Equalization), adaptive thresholding, edge detection, and watershed segmentation to preprocess the input MRI or CT scan images before applying YOLO for tumor localization and classification. Upon detecting a tumor, the system outputs key details such as the number and size (in pixels) of the tumors present. Furthermore, the system provides an automated email report containing the detection results, including processed images and tumor details, enabling healthcare professionals to receive timely information. The system also stores detection results in a database for record-keeping and future reference, enhancing the accessibility and management of patient data. The proposed system demonstrates a significant improvement in the speed and accuracy of brain tumor detection compared to traditional manual methods, providing a reliable tool for assisting healthcare professionals in diagnosing and treating brain tumors. This research highlights the potential of deep learning and image processing in advancing healthcare technology and improving patient outcomes
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 11
  • PageNo: 4636-4647

PROJECT IMPLEMENTATION ON : BRAIN TUMOR DETECTION USING IMAGE PROCESSING

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8.01 (Year 2024)
UGC Approved
Journal no 47859

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