1. What is the role of Convolution Neural Network in brain tumor detection?
Convolution Neural Network (CNN) plays a crucial role in brain tumor detection by utilizing computer-based procedures to detect tumor blocks and classify the type of tumor. It is a deep learning architecture that is commonly used for image processing and neural network techniques to improve the performance of detecting and segmenting brain tumors in MRI images. The CNN algorithm analyzes MRI images of different patients, identifies abnormal tissue growth, and determines the presence of a tumor. By accurately detecting and classifying brain tumors, CNN helps in early diagnosis and treatment, ultimately contributing to better patient outcomes.
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2. How does CNN-based image segmentation work?
CNN-based image segmentation works by processing 3-dimensional images, reducing dimensions to avoid underfitting, and using a deep convolution neural network with medical image architecture. The CNN model is trained with the BRATS dataset to detect brain tumors, exploiting both local and global features. The proposed approach differs from traditional CNNs by using a convolutional fully connected layer, resulting in a 40-fold speed increase. The method has shown successful results with higher accuracy and comparable performance to other approaches.
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