BRAIN TUMOR DETECTION IN HUMAN BEINGS USING ML

Abstract

This research paper presents a brain tumor detection system. It represents a major challenge in medical diagnostics, as early and precise detection improves patient outcomes. Conventional tumor identification approaches often rely on manual interpretation of medical examinations, which can be tedious and subject to human error. Deep learning-based algorithms have appeared in recent years as a practical method to automate and enhance brain tumor detection using medical imaging data. A Convolutional Neural Network (CNN) structure is proposed to attain a minimum accuracy of 97% and a maximum of 100%, utilizing its power to automatically learn hierarchical features from medical images that include Magnetic Resonance Imaging (MRI) scans. To learn distinctive features indicating tumor presence, the proposed CNN model is trained on a vast collection of labeled brain MRI images. The experimental results demonstrate that the proposed deep learning method is effective. The trained CNN performs well in distinguishing between tumor and non-tumor areas in brain scans. Additionally, cross-validation and unbiased assessments are employed to evaluate the model’s ability to generalize to unseen data. Deep learning in brain tumor detection holds the potential to significantly increase diagnostic accuracy, reduce human error, and accelerate decision-making. As research in deep learning continues to grow, future work might explore the integration of multi-modal imaging data, the use of transfer
learning, and ensemble strategies to strengthen the robustness and generalization of brain tumor detection systems. The proposed deep learning-based brain tumor detection system holds promise in improving medical professionals’ ability to correctly and promptly diagnose brain tumors, ultimately enhancing patient care and treatment outcomes.