Brain Tumor Segmentation and Classification using Optimized Deep Learning

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ID: 312947
2024
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Abstract
In the medical field, identifying brain tumors is a complex task that necessitates specialists meticulously analyzing MRI scans to detect tumors. Recent advancements have introduced various artificial intelligence methods aimed at automating this process. However, prior approaches often relied on singular datasets, which constrained their ability to identify brain cancers across diverse scenarios. This study addresses this issue by employing data augmentation and denoising algorithms on medical images from three distinct datasets, aiming to enhance detection efficiency. To evaluate the effectiveness of these methods, we implemented two deep learning algorithms utilizing Convolutional Neural Networks (CNNs), which demonstrated high accuracy. These results suggest that incorporating data augmentation and denoising techniques can significantly improve the accuracy of brain tumor diagnoses. This research contributes to ongoing efforts in the medical field to refine the application of advanced machine learning techniques for the early and precise detection of brain cancers.
Reference Key
imported_1777057998_69ebc0ce6936f Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sadaqat Ali Ramay
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/528
Keywords Keywords not found

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