Detection of Brain Tumour based on Optimal Convolution Neural Network

R Kishore Kanna; Susanta Kumar Sahoo; B K Mandhavi; V Mohan; G Stalin Babu; Bhawani Sankar Panigrahi

doi:10.4108/eetpht.10.5464

Authors

R Kishore Kanna Jerusalem College of Engineering
Susanta Kumar Sahoo Indira Gandhi Institute of Technology
B K Mandhavi Vardhaman College of Engineering
V Mohan Vardhaman College of Engineering
G Stalin Babu Aditya Institute of Technology and Management
Bhawani Sankar Panigrahi Vardhaman College of Engineering

DOI:

https://doi.org/10.4108/eetpht.10.5464

Keywords:

Deep Learning, Brain tumour, Diagnosis, CNN, MRI

Abstract

INTRODUCTION: Tumours are the second most frequent cause of cancer today. Numerous individuals are at danger owing to cancer. To detect cancers such as brain tumours, the medical sector demands a speedy, automated, efficient, and reliable procedure.

OBJECTIVES: Early phases of therapy are critical for detection. If an accurate tumour diagnosis is possible, physicians safeguard the patient from danger. In this program, several image processing algorithms are utilized.

METHODS: Utilizing this approach, countless cancer patients are treated, and their lives are spared. A tumor is nothing more than a collection of cells that proliferate uncontrolled. Brain failure is caused by the development of brain cancer cells, which devour all of the nutrition meant for healthy cells and tissues. Currently, physicians physically scrutinize MRI pictures of the brain to establish the location and size of a patient's brain tumour. This takes a large amount of time and adds to erroneous tumour detection.

RESULTS: A tumour is a development of tissue that is uncontrolled. Transfer learning may be utilized to detect the brain cancer utilizing. The model's capacity to forecast the presence of a cancer in a picture is its best advantage. It returns TRUE if a tumor is present and FALSE otherwise.

CONCLUSION: In conclusion, the use of CNN and deep learning algorithms to the identification of brain tumor has shown remarkable promise and has the potential to completely transform the discipline of radiology.

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