3D segmentation of brain tumour
by Rudresh Deepak Shirwaikar; Kruthika Ramesh; Abu Mohammed Faisal; M. Jeshwanth; Aditya Raghav
International Journal of Engineering Systems Modelling and Simulation (IJESMS), Vol. 15, No. 2, 2024

Abstract: Brain replacement is a grouping of abnormal cells in the brain. Brain tumours may be malignant or non-cancerous. Glioma, meningioma, and pituitary are common brain tumours. MRI scans may detect these cancers at various stages. There are many ways to classify and extract features from MRI brain tumour pictures. The CNN image classification approach accurately detects early-stage tumours. We first explore the 3D CNN using brain tumour segmentation. Then, we train several CNN architectures to compare their performance and design, in order to collect local and contextual data. One of the drawbacks of 3D design is memory use. 3D convolutions are computationally intensive and have exponential parameters. However, if correctly done, automatic identification of crucial traits without human supervision is conceivable. Its tremendous computational efficiency makes it the most often used design. Our main objective is to optimise memory consumption and processing to detect brain cancers in 3D MRI data. The 3D CNN architecture removes brain tumours first, then feeds them to a pre-trained feature extraction for CNN model. Using these extracted features, the best features are chosen by correlation-based output. This is done through feed-forward neural networks.

Online publication date: Fri, 01-Mar-2024

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