A Hybrid Deep Learning And Machine Learning Framework For Enhanced Brain Tumor Detection In MRI Using MobileNetV3 Features

Mr.Sachin .S.Bhosale; Dr. Anand Singh Rajawat; Dr.P.R.Bhaldare

A Hybrid Deep Learning And Machine Learning Framework For Enhanced Brain Tumor Detection In MRI Using MobileNetV3 Features

Author : Mr.Sachin .S.Bhosale, Dr. Anand Singh Rajawat, Dr.P.R.Bhaldare Journa Name: International Journal of Scientific Research & Engineering Trends Volume: 12 issue: 3 Year: Volume-12-issue-3 Views : 178

Abstract:

This study demonstrate the hybrid framework model that combine Machine Learning (ML) and Deep Learning (DL) techniques for the detection of brain tumor on MRI scan dataset. We employ MobileNetV3 for deep feature extraction via transfer learning, followed by classification using Logistic Regression (LR), SVM, Random Forest, KNN, and XGBoost. Experimental results demonstrate that Logistic Regression paired with MobileNet features achieved superior performance (Accuracy: 95.02%, Precision: 94.78%, Recall: 94.53%, F1-score: 94.58%), outperforming more complex classifiers. This indicates that MobileNet-derived features create a nearly linearly separable representation, positioning LR as an efficient and effective tool for automated, accurate brain tumor diagnosis, thereby augmenting clinical decision-making.