Brain Tumor Classification Based on Fine-Tuned Models and the Ensemble Method

Brain tumors are life-threatening for adults and children. However, accurate timely detection can save lives. This study focuses on three different types of brain tumors: Glioma, meningioma, pituitary tumors. Many studies describe the analysis classification tumors, but few have looked at problem feature engineering. Methods needed to overcome drawbacks manual diagnosis conventional feature-engineering techniques. An automatic diagnostic system is thus necessary extract features classify accurately. While progress continues be made, diagnoses still face challenges low accuracy high false-positive results. The model presented in this study, which offers improvements extraction classification, uses deep learning machine assessment Deep used encompasses application models such as fine-tuned Inception-v3 Xception. explored through deep- machine-learning algorithms including softmax, Random Forest, Support Vector Machine, K-Nearest Neighbors, ensemble technique. results these approaches compared with existing methods. has a test 94.34% achieves highest performance other recently reported improvement may sufficient support significant role clinical applications tumor analysis. Furthermore, type approach an effective decision-support tool radiologists medical diagnostics second opinion based magnetic resonance imaging (MRI) It also valuable time who manually review numerous MRI images patients.