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- W4385452511 abstract "The rapid growth of clinical imaging innovation in recent years has made it challenging for radiologists and clinical professionals to assess and categorize. Brain tumor image segmentation is a significant task in clinical image processing. Manually segmenting tumor growths for cancer detection from a large number of MRI (Magnetic Resonance Imaging) images produced in clinical daily practice is a tedious process. Early brain tumor identification is crucial for assessing treatment alternatives and enhancing patient survival. This necessitates the need to perform automatic brain tumor image segmentation. Clinical images offer an enormous amount of information that may be used to perform analysis, surgical preparation, planning, and research. As a result, a technique that can understand and separate the images is required. This study illustrates the influence of deep learning techniques in the brain tumor classification process and outlines the challenges that must be solved before deep learning can be successfully used to perform medical image classification tasks. This study highlights particular research contributions, datasets, and software/hardware used to overcome these challenges. This study has examined recent research articles. Deep learning approaches are currently used for resolving challenges in automated brain tumor segmentation. Simultaneously, current difficulties, datasets, performance assessment metrics, required hardware/software, and applications are briefly discussed. The article includes a survey of most effective deep learning methodologies as well as an agile description of traditional strategies. Deep learning algorithms may be regarded as the current standard for carcinoma classification due to their superior performance. CNNs and RNNs are the two most commonly used models." @default.
- W4385452511 created "2023-08-02" @default.
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- W4385452511 date "2023-06-01" @default.
- W4385452511 modified "2023-09-26" @default.
- W4385452511 title "A Review on Segmenting and Classification of Brain Tumor using Deep Learning Methods" @default.
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- W4385452511 doi "https://doi.org/10.1109/icces57224.2023.10192638" @default.
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