Brain-tumor-detection

The architecture was inspired by U-Net: Convolutional Networks for Biomedical Image Segmentation.

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overview

Data

The original dataset is from Kaggle: Br35H::Brain Tumor Detection 2020, and I've downloaded it and making PNG annotations from JSON annotation, and done the pre-processing.

Since the original dataset has 500 images for training and this number is small, I collected more data from different sources and generated more data using Flimimg.py. You can find it in /ImageSegmentation/MakeMoreData/ and use this dataset to train your custom model.

You can by using this site: Makesense annotate your data and use the JsonAnnotation_to_PNGAnnotation.py to make the PNG annotations. You can find it in /ImageSegmentation/AnnotationMaker/ .

This Dataset has been used to train these models.

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Model

Unet Architecture

This deep neural network is implemented with Keras functional API.

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Training

U-net

The U-net model is used to segment tumors in MRI images of the brain. After 10 epochs of training, the calculated accuracy is about 98%. Loss function for the training is basically just a binary crossentropy You can download my trained model from U-net

CNN

The CNN model is used to detect whether a tumor is there or not. After 15 epochs of training, the calculated accuracy is about 99.6%. Loss function for the training is basically just a binary crossentropy You can download my trained model from CNN

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How to use

Run detectBT.py

Or follow notebook and codes

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Results

Use the trained model to do segmentation on test images. The result is satisfactory.