The utilization of Artificial Intelligence in enabling the early detection of brain tumors

Diagnosing brain tumors is challenging due to their location and varied presentations, which may mimic common disorders. A cancer diagnosis can be missed even when advanced imaging is conducted due to interpretive error or an incompatible clinical history as presented. Machine learning, when applied to radiological imaging, can aid in alerting physicians of the presence of tumors and improve diagnostic evaluation. Enhanced evaluation of malignant tumors can lead to earlier detection and positively improve prognosis, quality of life, and treatment. We aimed to enhance brain tumor diagnosis using machine learning. In this study, we developed two machine learning models: a logistic regression model and a neural network model. We hypothesized that, while both of our techniques would demonstrate a high diagnostic accuracy, the neural network model would produce more successful results due to its greater complexity. Applying a dataset sourced from Kaggle, an online data science resource, into the algorithms demonstrated with test accuracies of 68% in the logistic regression model and 84% in the neural network model. Overall, the models suggested a promising future for machine learning applications to brain tumor diagnoses.