/Automatic-Optimal-N-Shot-learning-for-Brain-Tumor-Detection

Automatic Optimal N-Shot learning for Brain Tumor Detection

Primary LanguageJupyter Notebook

CDAC-DBDA-Project

Project Name: Automatic Optimal N-Shot learning for Brain Tumor Detection

This repository consists of all the files that are required for our project (Automatic Optimal N-Shot learning for Brain Tumor Detection)

A Brain tumor is considered as one of the aggressive diseases, among children and adults. Proper treatment, planning, and accurate diagnostics needs to be implemented to improve the life expectancy of the patients. The best technique to detect brain tumors is Magnetic Resonance Imaging (MRI). A huge amount of image data is generated through the scans. These images are examined by the radiologist. A manual examination can be error-prone due to the level of complexities involved in brain tumors and their properties. Despite deep convolutional neural networks achieved impressive progress in medical image computing and analysis, its paradigm of supervised learning demands a large number of training images and complex hyperparameter optimization processes. In clinical practices, collection of huge number of MRIs is difficult to acquire. We have developed a deep learning model with few-shot learning and Automatic Hyperparameter Optimization for brain tumor detection.

Dataset

‣ Brain Tumor Detection (250 Images)

‣ Brain Tumor Detection (2759 Images)

Technology used

  • Python
  • Machine Learning
  • Deep Learning
  • Few Shot Learning
  • Optimization
  • Optuna
  • Raytune
  • Scikit-Optimize
  • Mahotas
  • Jupyter Notebook
  • Google Colab

Acknowledgement

This project “Automatic Optimal N-Shot learning for Brain Tumor Detection” was a great learning experience for us and we are submitting this work to Advanced Computing Training School (CDAC ACTS).

We all are very glad to mention the name of Mr. Ramesh Naidu for his valuable guidance to work on this project. His guidance and support helped us to overcome various obstacles and intricacies during the course of project work.

Our most heartfelt thanks goes to Ms. Uma Prasad (Course Coordinator, PG-DBDA) who gave all the required support and kind coordination to provide all the necessities like extra Lab hours to complete the project and throughout the course up to the last day at C-DAC ACTS, Bangalore.