--- a
+++ b/README.md
@@ -0,0 +1,59 @@
+# Brain tumor segmentation in MRI images using U-Net
+
+Here, I have implemented a U-Net from the paper ["U-Net: Convolutional Networks for Biomedical
+Image Segmentation"](https://arxiv.org/pdf/1505.04597.pdf) to segment tumor in MRI images of brain.
+
+There are 3 types of brain tumor:
+1. meningioma
+2. glioma
+3. pituitary tumor
+
+## Examples of predicted tumor segment by the current U-Net implementation.
+meningioma       	  | glioma		| 	pituitary tumor             
+:-------------------------:|:-------------------------:|:------------------------:
+![](samples/sample1.png)  |  ![](samples/sample2.png)		| ![](samples/sample3.png)
+![](samples/sample4.png)  |  ![](samples/sample5.png)		| ![](samples/sample6.png) 
+![](samples/sample7.png)  |  ![](samples/sample8.png)		| ![](samples/sample9.png) 
+
+## Getting Started
+Here I will explain how to get the data and convert it into the usable form. You can run the train and run model using [notebook](https://github.com/adityajn105/brain-tumor-segmentation-unet/blob/master/brain-tumor-segmentation.ipynb).
+
+### Prerequisites
+You will need Python 3.X.X with some packages which you can install direclty using requirements.txt.
+> pip install -r requirements.txt
+
+### Get the Dataset
+I have used brain-tumor segment dataset which is available on the internet. You can run [download_data.sh](https://github.com/adityajn105/brain-tumor-segmentation-unet/blob/master/download_data.sh) shell script to download all data. It contains 3064 MRI images and 3064 masks. 
+> bash tumor-segmentation-unet/download_data.sh
+
+After that run the following command to convert data in useable form.
+> python tumor-segmentation-unet/mat_to_numpy.py brain_tumor_dataset/ 
+
+## Model Architecture
+I have used combination of multiple losses which includes binary crossentropy, dice loss with equal weightage. Also I have used Conv2D transpose layers for upsampling. 
+
+I have used the metric called IOU (Intersection over Union) metric to track progress of training and trained Unet with Adam optimizer for 40-60 epochs with decaying learning rate between 1e-3 to 1e-4. I have also performed only one Image augmentation i.e. horizontal flip. Train and test split was stratified using type of tumor.
+
+![Performance](screenshots/performance2.png)
+
+
+Detailed architecure is given below.
+![Unet Architecture](screenshots/unet-tumor-seg.png)
+
+## Possible Improvements
+1. Can use transfer learning to utilize state-of-the-art model like VGG, Inception, Resnet.
+2. We can use more types of image augmentation like vertical flip, brightness, zoom etc.
+3. Include lovasz loss with higher weightage.
+4. Learn and use Hypercolumns
+
+## Author:
+* Aditya Jain : [Portfolio](https://adityajain.me)
+
+## To Read:
+1. [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf)
+2. [Image Segmentation, ConvNet, FCN, Unet](https://towardsdatascience.com/understanding-semantic-segmentation-with-unet-6be4f42d4b47)
+3. [Up-sampling with Transposed Convolution](https://towardsdatascience.com/up-sampling-with-transposed-convolution-9ae4f2df52d0)
+4. [Lovasz Loss](https://arxiv.org/abs/1705.08790) 
+5. [Jaccard Index - Intesection over Union](https://www.jeremyjordan.me/evaluating-image-segmentation-models/)
+6. [Understanding Dice Loss](https://forums.fast.ai/t/understanding-the-dice-coefficient/5838)
+7. [Another Image Segmentation Problem](https://github.com/adityajn105/TGS-Salt-Identification-Image-Segmentation-)
\ No newline at end of file