Abstract
A brain tumor is one of the conditions in which a patient's brain develops abnormal cells. They are called tumors and there are several varieties of them. In this paper, brain tumor segmentation and detection methods are being developed that use images from an MRI series as input images to identify the type of tumor. The use of a convolutional neural network (CNN) model to recognize brain tumors from X-ray images pictures is described in this paper. This investigation was carried out with the help of Google Collab. The MRI images of brain tumors are categorized into glioma, meningioma, and pituitary, and no tumor is one such difficult problem. In this work, the datasets comprise 388 images for testing purposes. With an accuracy of 70%, the projected arrangement achieves a commendable performance. The successful conclusion demonstrates the suggested algorithm’s ability to classify brain tumors.
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Upadhyay, A., Tiwari, N., Singh, R. (2023). Brain Tumor Recognition and Classification of MRI Images Using a Convolutional Neural Network Model. In: Rani, A., Kumar, B., Shrivastava, V., Bansal, R.C. (eds) Signals, Machines and Automation. SIGMA 2022. Lecture Notes in Electrical Engineering, vol 1023. Springer, Singapore. https://doi.org/10.1007/978-981-99-0969-8_38
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DOI: https://doi.org/10.1007/978-981-99-0969-8_38
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