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Enhancement of Brain Tumor Scan Through AI Segmentation

  • Unique Paper ID: 166569
  • Volume: 11
  • Issue: 2
  • PageNo: 1394-1402
  • Abstract:
  • Our main goal is to provide an overview of the enhancement of brain tumor scans utilizing AI segmentation algorithms. We will be able to examine how artificial intelligence (AI) can enhance the segmentation of brain tumors in medical scan images with the major assistance of this review. The main goal is to evaluate the contribution of CNN-based methods to the precise segmentation of brain tumor areas from medical imaging data. Our goal is to create a CNN model that will improve image quality and increase the precision with which tumors can be identified. The review is justified by the urgent need for precise and effective techniques, with a special emphasis on the application of Convolutional Neural Network (CNN) models. 1. This research employs several CNN architectures and approaches to increase tumor segmentation accuracy and dependability. They frequently use preprocessing techniques to get the input data ready, and then CNNs are applied to extract features and segment the data. 2. Taking a look at the earlier research, we can say the following about CNNs to improve the brain scan: 3. CNN's proficiency in identifying the salient features from brain images allows us to more precisely depict the tumor's location. 4. Transfer learning is a neat technique in which we take a sophisticated model with extensive image knowledge and train it to understand brain tumors. 5. When compared to the previous methods, the metrics we use to assess the quality of our tumor drawing (such as DSC, sensitivity, and specificity) are typically higher when we utilize CNNs.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 2
  • PageNo: 1394-1402

Enhancement of Brain Tumor Scan Through AI Segmentation

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