Akyol, KemalAkyol K.Akyol, K2023-05-092023-05-092022-09-012022-09-012022.01.012662-4729https://hdl.handle.net/20.500.12597/12218Brain tumours are life-threatening and their early detection is very important in a patient's life. At the present time, magnetic resonance imaging is one of the methods used for detecting brain tumours. Expert decision support systems serve specialist physicians to make more accurate diagnoses by minimizing the errors arising from their subjective opinions in real clinical settings. The model proposed in this study detects important keypoints and then extracts hypercolumn deep features of these keypoints from some convolutional layers of VGG16. Finally, Random Forest and Logistic Regression classifiers are fed with a set of these features. Random Forest classifier offered the best performance with 94.51% accuracy, 91.61% sensitivity, 8.39% false-negative rate, 97.42% specificity, and 97.29% precision using fivefold cross-validation in this study. Consequently, it is thought that the proposed model could contribute to field experts by integrating it into computer-aided brain magnetic resonance imaging diagnosis systems.Brain tumours are life-threatening and their early detection is very important in a patient's life. At the present time, magnetic resonance imaging is one of the methods used for detecting brain tumours. Expert decision support systems serve specialist physicians to make more accurate diagnoses by minimizing the errors arising from their subjective opinions in real clinical settings. The model proposed in this study detects important keypoints and then extracts hypercolumn deep features of these keypoints from some convolutional layers of VGG16. Finally, Random Forest and Logistic Regression classifiers are fed with a set of these features. Random Forest classifier offered the best performance with 94.51% accuracy, 91.61% sensitivity, 8.39% false-negative rate, 97.42% specificity, and 97.29% precision using fivefold cross-validation in this study. Consequently, it is thought that the proposed model could contribute to field experts by integrating it into computer-aided brain magnetic resonance imaging diagnosis systems.falseBrain magnetic resonance imagingDeep learningHypercolumn deep featuresKeypoint detectionBrain magnetic resonance imaging | Deep learning | Hypercolumn deep features | Keypoint detectionJournal Article10.1007/s13246-022-01166-810.1007/s13246-022-01166-82-s2.0-85136596691WOS:000843438700004359979262662-4737