BRAIN TUMOR DETECTION AND CLASSIFICATION USING DEEP LEARNING

Information Science And Engineering

Brain tumors play a crucial role in contemporary medical diagnostics. This abstract summarizes an searching into the application of Convolutional Neural Networks for the automatically identifying and categorizing brain tumors, primarily utilizing MRI scans. CNNs, known for their prowess in computer tasks, are increasingly making a offering a paradigm shift in the way we approach brain tumor diagnosis. the practical implementation of CNNs, designed to process MRI images, extract salient features, and precisely categorize brain tumors into various types, such as gliomas, meningiomas, and pituitary tumors. The methodology is developed, trained, and tested on of MRI scans and adapt to the intricate patterns present in different tumor types. The use of convolutional neural networks (CNNs) offers several advantages, including the potential for enhancing diagnostic accuracy, reducing human errors. These enhancements are crucial for prompt medical responses. ensuring that patients receive the most appropriate treatment options promptly. The study showcases the promising role of CNNs in augmenting medical imaging diagnostics, ultimately contributing to better patient outcomes and further making strides in the field of neuro-oncology.

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