673958639/Convolutional-Neural-Network-for-Brain-Lesion-Segmentation-in-MRI-Images
In this work, four popular deep convolutional neural networks (U-NET, DeepLab, FCN and SegNet) for image segmentation are constructed and compared. This comparison reveals the tradeoff between achieving effective segmentation and segmentation accuracy. Using deep learning, specifically convolutional neural network methods, to build and train models
This project helps radiologists and medical researchers automatically identify and outline brain lesions in MRI scans. By inputting raw MRI images, it produces segmented images highlighting the lesion areas, aiding in diagnosis and research. The primary users are medical professionals or researchers working with brain MRI data.
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Use this if you need to accurately segment and identify brain lesions from MRI images to assist in medical diagnosis or research.
Not ideal if you are looking to segment other types of medical images or non-medical images, as it is specifically designed and trained for brain lesion segmentation in MRIs.
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Nov 21, 2022
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