Abstract
Tumour is the assortment or mass growth of abnormal cells within the brain. Individuals are still dying because of brain tumour. So early and accurate detection of brain tumour will scale back the death rate. The computer-aided application will help to give accurate detection of brain tumour. The process of performing some operations on image to get useful information is called image processing. At present, image processing is rapidly growing technology. It consists of many types of imaging methods, like MRI, CT scans, X-rays. By this, we can find small abnormalities in the human body and brain. The main process of image processing is to extract accurate information from the image. In this paper, the GLCM texture feature and Haralick texture features of the images are extracted. Then, the calculated features are given as an input to various machine learning classifiers to classify the MRI images of the brain. This work carried out with three steps, preprocessing, feature extraction, and classification. Finally, the methods are compared, and it has been found that MLP is the best accuracy classifier.
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Shanmuga Priya, S., Saran Raj, S., Surendiran, B., Arulmurugaselvi, N. (2021). Brain Tumour Detection in MRI Using Deep Learning. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_38
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DOI: https://doi.org/10.1007/978-981-15-5788-0_38
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