Brain tumor segmentation and classification using deep learning
Brain tumors are one of the fatal diseases in today?s world, there is only 3%survival rate from brain tumor. World health organization (WHO) has observed that the increase in the use of radiofrequency, magnetic fields gadgets are linked with carcinogens. Treatment options for brain cancer depend on
Project Title
Brain tumor segmentation and classification using deep learning
Project Area of Specialization
Project Summary
Brain tumors are one of the fatal diseases in today’s world, there is only 3%survival rate from brain tumor. World health organization (WHO) has observed that the increase in the use of radiofrequency, magnetic fields gadgets are linked with carcinogens. Treatment options for brain cancer depend on the tumor's location, the degree to which the tumor is affecting brain and spinal cord functions, and the patient’s health history. Some treatment options include chemotherapy, radiation therapy, surgery, ancillary therapeutic agents, and resection but these treatment options are not well effective so in today’s world of artificial intelligence we have to find ways that can be helpful to the doctors and are time, money savings, so segmentation and classification with deep learning and convolutional neural network might help the doctors in detection and the cure to these tumors.
Project Objectives
The basic purpose of the project is to provide facility to the doctors .After diagnosing some abnormality one couldn't surely tell either its benign or malignant tumor.
- To recognize the glioma region from MRI.
- Providing accurate results through deep learning and convolutional neural network.
Project Implementation Method
The project implementation method involves the SVM and Decision tree algorithm in MATLAB using nasnet mobile and Inception methods. Basically we will classify the dataset either benign or malignant, for that purpose we will run the dataset in matlab under Decision tree or Support vector machine algiorthms and will train the module using nasnet methods and more.
Benefits of the Project
It is difficult for doctors to recognize the glioma region accurately through Magnetic resonance imaging (MRI) Our aim is accurate and automatic recognition of glioma region from the brain . So, We want to have our part to save others as much as we can from this disease to help doctors to detect this disease on time
Technical Details of Final Deliverable
The final details will include:
- a fully trained model to identify brain tumor with accracy of 95% and above.
- a prototype for the model.
- FYP docmentation including 8 chapters and complete details related to the project.
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) |
|---|---|---|---|---|
| GeForce GTX 1660 super twin fan ZT-T16620-10L Graphic Card | Equipment | 1 | 65000 | 65000 |
| stationery | Miscellaneous | 1 | 8000 | 8000 |
| Total in (Rs) | 73000 |
If you need this project, please contact me on contact@adikhanofficial.com
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