Brain Tumour Detection Using Deep Learning

ABSTRACT:

The motivation behind this study is to detect brain tumour and provide better treatment for the sufferings. The abnormal growths of cells in the brain are called tumours and cancer is a term used to represent malignant tumours. Usually CT or MRI scans are used for the detection of cancer regions in the brain. Positron Emission Tomography, Cerebral Arteriogram, Lumbar Puncture, Molecular testing are also used for brain tumour detection. In this study, MRI scan images are taken to analyse the disease condition. Objective this research works are i) identify the abnormal image ii) segment tumour region. Density of the tumour can be estimated from the segmented mask and it will help in therapy. Deep learning technique is employed to detect abnormality from MRI images. Multi level thresholding is applied to segment the tumour region. Number of malignant pixels gives the density of the affected region.

ALGORITHM / MODEL USED:

CNN Model Architecture

PROJECT OUTPUT VIDEO:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

• System : Pentium i3 Processor.
• Hard Disk : 500 GB.
• Monitor : 15’’ LED
• Input Devices : Keyboard, Mouse
• Ram : 4 GB

SOFTWARE REQUIREMENTS: 

• Operating system : Windows 10.
• Coding Language : Python 3.8
• Web Framework : Flask

REFERENCE:

Avigyan Sinha, Aneesh R P, Malavika Suresh, Nitha Mohan R, Abinaya D, Ashwin G Singerji, “Brain Tumour Detection Using Deep Learning”, IEEE Conference, 2021.