ANALYSIS OF BRAIN TUMOR MENINGIOMA DETECTION SYSTEM DEVELOPMENT USING CONVOLUTIONAL NEURAL NETWORK METHOD MOBILENET ARCHITECTURE

This research aims to design a brain tumor detection tool using the MobileNet architecture Convolutional Neural Network method. The CNN method with MobileNet can effectively detect brain tumors via CT-Scan, with more accurate diagnostic results and reduced errors. This method also speeds up diagnostic time and can help remote areas. The MobileNet application is standalone but requires a web server; it can detect meningioma and glioma brain tumors. The training data includes contrast and non-contrast images, with an accuracy level of MobileNet version 3 reaching 100% compared to the Anatomical Pathology examination. Evaluation of the effectiveness of the CNN method provides an understanding of the strengths and weaknesses of this method. The CNN method can potentially improve diagnostic accuracy, time efficiency, and the results of detecting meningioma brain tumors. Analysis of differences in diagnoses before and after using the CNN method provides essential information about the benefits and advantages of its use in clinical practice, including improvements in detection accuracy, sensitivity, and specificity in identifying meningioma brain tumors with consistent and reliable results.