Machine learning approach to brain tumor detection and classification

TL;DR

This paper evaluates various statistical and machine learning models, notably CNNs, for brain tumor detection and classification using MRI images, demonstrating CNN's superior performance in multi-class diagnosis.

Contribution

The study systematically compares multiple models, highlighting CNN's effectiveness for accurate brain tumor detection and multi-class classification in medical imaging.

Findings

CNN outperforms other models in accuracy

CNN successfully classifies four tumor categories

Machine learning models are effective for early diagnosis

Abstract

Brain tumor detection and classification are critical tasks in medical image analysis, particularly in early-stage diagnosis, where accurate and timely detection can significantly improve treatment outcomes. In this study, we apply various statistical and machine learning models to detect and classify brain tumors using brain MRI images. We explore a variety of statistical models including linear, logistic, and Bayesian regressions, and the machine learning models including decision tree, random forest, single-layer perceptron, multi-layer perceptron, convolutional neural network (CNN), recurrent neural network, and long short-term memory. Our findings show that CNN outperforms other models, achieving the best performance. Additionally, we confirm that the CNN model can also work for multi-class classification, distinguishing between four categories of brain MRI images such as normal, glioma, meningioma, and pituitary tumor images. This study demonstrates that machine learning approaches are suitable for brain tumor detection and classification, facilitating real-world medical applications in assisting radiologists with early and accurate diagnosis.