Brain Pathology Detection with CNN

Brain pathology detection is a crucial task in medical imaging analysis for early detection of brain diseases that can significantly improve patient outcomes. In the Brain Pathology project, a deep learning model using convolutional neural networks (CNNs) is developed to detect brain pathologies from MRI images. The model, based on VGG-16 architecture, achieves an accuracy of 90% on the test set and 99% on the val set. The project provides the code and instructions to train, test, and deploy the model as a Flask web application. The application allows users to upload a brain MRI image and get a prediction on whether the image is normal or abnormal.

Description

Brain pathology use the VGG-16 model for transfer learning, which is trained on the ImageNet dataset. The model is fine-tuned on a custom dataset of brain images with and without pathology. The dataset consists of 5000 images. The model was trained on Kaggle and deployed on Flask. This project can be used as a starting point for building a more sophisticated medical image classification system.

Dependencies

The following packages are required to run the code:

• Python: This is the programming language in which the FaceAB software is written. You can download and install Python from here.

> python --version
Python 3.9.1

Requisites

1. Python:

   • Python: an interpreted, high-level, general-purpose programming language.
   • TensorFlow: an open-source software library for dataflow and differentiable programming across a range of tasks.
   • Keras: a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.
   • Flask: a lightweight WSGI web application framework.
   • OpenCV: a library of programming functions mainly aimed at real-time computer vision.
   • Matplotlib: a comprehensive library for creating static, animated, and interactive visualizations in Python.
   
   All dependencies can be installed using pip:

   pip install -r requirements.txt
   

Future Work

In the future, there are several opportunities for further development and improvement of this brain pathology classification project, such as:

• Expanding the dataset used for training the model, as a larger and more diverse dataset can potentially improve the model's accuracy and ability to generalize to new data.
• Exploring more advanced image preprocessing techniques to further enhance the quality of the input images for the model.
• Experimenting with different neural network architectures and hyperparameters to optimize the model's performance.

Concluding

This project accurately classifies brain images into healthy and unhealthy categories with a high degree of accuracy. The success of this project can be attributed to the following key points:

• Extensive preprocessing techniques applied to the images
• Careful selection of the VGG-16 model as the classification algorithm

In the future, the project can be improved by considering the following opportunities:

• Expanding the dataset to include a wider range of brain diseases and conditions

• Incorporating other techniques such as transfer learning and data augmentation to further improve the accuracy of the model

In conclusion, the Brain Pathology project serves as a valuable tool in the detection and diagnosis of brain diseases and has the potential to make a significant impact in the field of medical imaging.

Reference

The following are the references used in the development of this project:

• Keras. (n.d.). Keras documentation. Retrieved from https://keras.io/api/
• OpenCV. (n.d.). OpenCV documentation. Retrieved from https://docs.opencv.org/master/
• TensorFlow. (n.d.). TensorFlow documentation. Retrieved from https://www.tensorflow.org/api_docs
• Scikit-image. (n.d.). Scikit-image documentation. Retrieved from https://scikit-image.org/docs/stable/api/api.html