Efficient Classification Of Brain Tumors Images Using Neural Network Technique
| Main Authors: | R.Navin Kumar M.C.A.,M.Phil., S.Raveenthiran |
|---|---|
| Format: | Article Journal |
| Bahasa: | eng |
| Terbitan: |
, 2022
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/6397160 |
Daftar Isi:
- Using biopsy, brain tumors classification is performed, which is not normally conducted before definitive brain surgery. The technology improvement and machine learning helps radiologists for diagnostics of tumor without invasive measures. Convolutional neural network (CNN) is the machine-learning algorithm which achieved substantial results in image classification and segmentation. Some of the most notable primary brain tumors are meningiomas, gliomas and pituitary tumors. Gliomas is a general term for tumor which arise from the brain tissues other than the nerve cells and the blood vessels. But, meningiomas arise from membranes that cover brain and surround central nervous system, whereas pituitary tumors are the lumps that sit inside skull. Most notable important difference between these three types is that meningiomas are generally benign, and gliomas are commonly malignant. This project deevlops a new CNN architecture to classify brain tumor types. With i) good generalization capability and ii) good execution speed, newly developed CNN architecture are being used as an effective decision-support tool for radiologists in diagnostics. Python is used for development of the project