EFFICIENT CLASSIFICATION OF BRAIN TUMOR IMAGES USING NEURAL NETWORK TECHNIQUE

Authors

  • Ms.T.Kokilavani, Dr. K. Ganesh Kumar, N.Kiruthika, G.Mathumitha, W.Merlin Sweatha, S.Monisha

Keywords:

Deep Learning, Neural Network, Brain Tumor, MRI Images

Abstract

Using biopsy, the classification of brain tumors is performed, which isn't usually conducted before definitive surgical process. the development of technology and machine learning helps radiologists in diagnostics of tumor without invasive measures. convolutional neural network (CNN) could be a machine-learning algorithm that has achieved substantial leads to image segmentation and classification. a number of the first brain tumors are gliomas, meningiomas, and pituitary tumors. Gliomas are a general term for tumors that arise from brain tissues apart from nerve cells and blood vessels. On the opposite hand, meningiomas arise from the membranes that cover the brain and surround the central systemanervosum, whereas pituitary tumors are lumps that sit inside the skull. the foremost important difference between these three forms of tumors is that meningiomas are typically benign, and gliomas are most ordinarily malignant. This project presents a replacement CNN architecture for brain tumour classification of tumor types. With good generalization capability and good fastness, the new developed CNN architecture may well be used as an efficient decision-support tool for radiologists in medical diagnostics. Python 3.7 is employed to develop the project

 

Published

2022-06-29

How to Cite

Ms.T.Kokilavani, Dr. K. Ganesh Kumar, N.Kiruthika, G.Mathumitha, W.Merlin Sweatha, S.Monisha. (2022). EFFICIENT CLASSIFICATION OF BRAIN TUMOR IMAGES USING NEURAL NETWORK TECHNIQUE. International Journal of Advanced Engineering Science and Information Technology, 10(6), 35–41. Retrieved from http://ijaesit.com/index.php/home/article/view/88