Comparative Study of Machine Learning Based Tumor Detection Techniques in Human Brain

Kaur, Dapinder; Ummat, Aman; Kaur, Sandeep

doi:10.1109/iccmst54943.2021.00034

Cited by 1 publication

(1 citation statement)

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“…Deep learning model performance can be affected by size differences. The computational demands of the model can be reduced by reducing the image size, allowing the analysis of more images in a shorter period of time [19,20].…”

Section: Resizingmentioning

confidence: 99%

Detection of Brain Tumour based on Optimal Convolution Neural Network

Kishore Kanna,

Sahoo,

Mandhavi

et al. 2024

EAI Endorsed Trans Perv Health Tech

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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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Section: Resizingmentioning

confidence: 99%