Abstract
This research illustrates a method for detecting and classifying brain tumors using machine learning algorithms. The preprocessing of the MRI images is done to improve the image quality by reducing the effects of noise, increasing the contrast and skull stripping. The Otsu thresholding algorithm with morphological operations is then applied to segment the tumor region from the surrounding healthy brain tissues. Later, a convolutional neural network (CNN) architecture has been proposed by us that can classify the segmented tumor images as benign, malignant, or normal. This research contributes to the development of a more effective and efficient brain tumor detection and classification system for medical use.
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Roopa, K.R., Sindagikar, S., Kalkod, P.G., Vishnu, P.M., Lata (2023). Brain Tumor Detection and Classification. In: Yadav, A., Nanda, S.J., Lim, MH. (eds) Proceedings of International Conference on Paradigms of Communication, Computing and Data Analytics. PCCDA 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4626-6_30
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DOI: https://doi.org/10.1007/978-981-99-4626-6_30
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