A Multi-Class Brain Tumor Classifier using Convolutional Neural Network with 99% Accuracy achieved by applying the method of Transfer Learning using Python and Pytorch Deep Learning Framework
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Updated
Apr 15, 2024 - Jupyter Notebook
A Multi-Class Brain Tumor Classifier using Convolutional Neural Network with 99% Accuracy achieved by applying the method of Transfer Learning using Python and Pytorch Deep Learning Framework
Brain Tumor Detection from MRI images of the brain.
Classifying Three Types of Brain Tumor
Brain tumors are the consequence of abnormal growths and uncontrolled cells division in the brain. They can lead to death if they are not detected early and accurately. Some types of brain tumor such as Meningioma, Glioma, and Pituitary tumors are more common than the others.
Brain Tumor Detection using CNN: Achieving 96% Accuracy with TensorFlow: Highlights the main focus of your project, which is brain tumor detection using a Convolutional Neural Network (CNN) implemented in TensorFlow. It also emphasizes the impressive achievement of reaching 96% accuracy, which showcases the effectiveness of your model.
A thorough project on brain tumour diagnosis. From start; data collection to finish; mobile app development.
Segmentation of Tumor Region in the Brain
Machine Learning
Brain tumor Detection and Classification using Magnetic Resonance Images
2D-CNN for Brain Tumor Classification
Flutter app to detect brain tumor from MRI scans
A mobile application developed for Brain tumor detection
A pattern classification analysis tool that potentially increased brain tumor diagnostic procedures. By taking an information picture, assign significance to different viewpoints in the picture and classify each case.
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