BRAIN TUMOR DETECTION
Mr.S.PRUDHVIRAJ 1, K.SIDDHARDHA2, K.KEERTHANA 3, K.BHAVANI4,R.SAI SANTOSH PRAVEEN5,
1Assistant Professor , Dept of Artificial Intelligence & Machine Learning
2,3,4,5 Sreyas Institute of Engineering and Technology
ABSTRACT
Brain tumor detection is a significant problem in medical diagnostics since early and accurate detection improves patient outcomes. Traditional tumor identification techniques often depend on manual interpretation of medical examination, which can be time-consuming and prone to human error. Algorithms based on deep learning have emerged in recent years as a viable way to automate and enhance brain tumor identification using medical imaging data. This paper conveys an extensive look on the use of deep learning for brain tumor identification. A Convolutional Neural Network(CNN) architecture is put forward to reach minimum accuracy of 97% and maximum of 100%, using its abilities to automatically learn hierarchical attributes from medical imagery that involve Magnetic Resonance Imaging(MRI) scans. To learn discriminative features suggestive of tumor presence, the suggested CNN framework is trained an extensive collection of labeled brain MRI images. The findings from experiments show that the proposed deep learning approach works. The trained CNN is quite good at differentiating between tumor and non-tumor regions in brain scans. Furthermore, cross-validation and unbiased evaluation are used to assess the model’s capacity to generalize to data that was previously unavailable. Deep learning in brain tumor identification has the potential to greatly enhance diagnostic accuracy, reduce human error, and speed up decision-making. As deep learning research advances, future studies may look at the amalgamation of multi-modal imaging data, transfer learning, and ensemble techniques in order to boost the robustness and generalizability of brain tumor diagnosis. The proposed deep learning- based brain tumor detection system offers the potential for improving medical professionals’ capacity to properly and instantly diagnose brain tumors, ultimately leading to improvements in patient care and outcomes.
Keywords: Brain Tumor detection, Diagnosis, Deep Learning, Convolutional Neural Networks, Pooling, MRI Dataset
