Multi-Modal Case Study on MRI Brain Tumor Detection Using Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbor, Temporal Convolution & Transfer Learning

In the Medical field, Brain Tumor Detection has become a critical and demanding task because of its several shapes, locations, intensity image. That’s why an automated system is important to aid physicians radiologists in detecting classifying brain tumors. this research, we have discussed different machine learning as well deep algorithm which are mostly used for image classification. We also compared models that being tumor classification based on learning. MRI images Glioma tumor, Pituitary Meningioma base techniques along with accuracy using those images. pre-trained training Those provided outstanding performance less power consumption computational time. EfficientNet-B3 best 98.16% among other traditional algorithms. The experimental result model proven most efficient detection comparison recent studies.