Raspbery Pi based embedded system for Brain Tumor Segmentation by using Deep learning
In this project, we present a handheld device to facilitate the medical specialist for automatic brain tumor detection from Magnetic resonance images (MRI) that based on Deep Neural Networks (DNNs). The proposed approach works on both low and high level/grade images of Brain. The reasons that motiva
Project Title
Raspbery Pi based embedded system for Brain Tumor Segmentation by using Deep learning
Project Area of Specialization
Project Summary
In this project, we present a handheld device to facilitate the medical specialist for automatic brain tumor detection from Magnetic resonance images (MRI) that based on Deep Neural Networks (DNNs). The proposed approach works on both low and high level/grade images of Brain. The reasons that motivate us is the outstanding performance of medical technology is grooming vastly in this field. The machine learning solution are very outstanding and their performance provide the results that are very close to the expert opinions
Project Objectives
- Embedding an artificial intelligence-based solution in a computer based handheld device for brain tumor segmentation and classification using Deep Neural, to support our medical industry.
- By providing medical staff a handheld device that are going to ease the way of treatment and learning about brain tumors and infections very easily.
- Local hospital use this device easily that may help in learning and brain tumor diagnosis becomes easy without experts.
Project Implementation Method
- Gathered and download dataset of brain MRI from a brats challenge (2015)
- Train a model using Convolutional Neural Network (CNN) based network to classify and predict different segments of brain MRI
- Deploy trained network on Raspberry Pie
Benefits of the Project
- Proposed method for brain tumor classification and segmentation for disease screening in order to overcome the burden on specialist and early diagnosis of serious problems before it will affect the activities of brain.
- Efficient way of detecting and classifying brain tumor.
- Manageable and Convenient tool for medical staff.
Technical Details of Final Deliverable
The technical details of this project comprise on the following two requirements that are given below.
Software Requirements:
- Python
- Anaconda
Hardware Requirements:
- Raspberry Pie
- Touch Screen LED
- Heat sink fan kit
- Rx 2080 GPU for Deep learning model training
Final Deliverable of the Project
Core Industry
Other Industries
Core Technology
Other Technologies
Sustainable Development Goals
Required Resources
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| 7 | Equipment | 1 | 5240 | 5240 |
| Raspberry Pie Protection Case | Equipment | 1 | 3200 | 3200 |
| Raspberry Pie 4 | Equipment | 1 | 13199 | 13199 |
| Gtx 1070 GPU | Equipment | 1 | 47990 | 47990 |
| Miscellaneous expenses | Miscellaneous | 1 | 8000 | 8000 |
| Total in (Rs) | 77629 |
If you need this project, please contact me on contact@adikhanofficial.com
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