Brain Tumor Classification with Efficient Net Convolutional Neural Network (CNNs) and Grad-CAM Visualization
In this project we will build and train an Efficient Net model and apply it to the Brain Tumor MRI Dataset to classify tumors: glioma_tumor, meningioma_tumor, pituitary_tumor, and no_tumor.
In addition, we will also use a technique known as Gradient-Weighted Class Activation Mapping (Grad-CAM) to visualize the regions of the inputs and help us explain how our CNN models think and make decision.
The data set which we are going to use has 3,285 images of brain MRI scans Which are categorized in four different classes namely glioma_tumor, meningioma_tumor, pituitary_tumor, and no_tumor.
The dataset can be accessed on Kaggle Brain Tumor MRI Dataset or you can clone the dataset from this github repository.
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