Enhancing Brain Tumor Classification with Advanced Machine Learning Techniques: A Study on Glioma, Meningioma, and Brain Tumor MRI Images

Abstract

Brain tumors (glioma, meningioma and other simple brain tumer) are considered as the most complex and the dangerous conditions that affects human brain. Precise classification of the brain tumors is very important for determining and appropriate diagnosis as well as therapy. Early and accurate identification plays an important role in increasing the treatment outcomes and also the survival rates. MRI imaging is a best way to diagnose brain tumors and new possibilities are availed to enhance the accuracy in classification of brain tumers by the advanced machine learning techniques.

The accuracy and automation in classification of medical conditions like brain tumers is highly revolutionized with help of Data science(particularly machine learning). In this paper the use of transfer learning models including InceptionV3, VGG-16, Xception, ResNet50, MobileNet, and EfficientNet were used to evaluate dataset and their effectiveness in the classification of  brain tumors was compared. These models used CNN as base model and identified higher-order features in the MRI images of brain tumers and provided more reliable and efficient brain tumor classification. The integration of machine learning improves diagnostic accuracy and also reduces the required time for analysis, supporting medical specialists in their diagnosis process.

A detailed methodology was followed for testing the effectiveness of different machine learning techniques in the brain tumor classification. Initially we trained transfer learning models on MRI images of glioma, meningioma, and typical brain tumors. MobileNet was the top-performing model, as it achieves a maximum training accuracy of 99.15%, and a testing accuracy of 92.13% as well as an ROC AUC score of 0.9610. Ensemble methods, especially Bagging, were used to improve the results of  the classification performance. The Bagging ensemble combined with MobileNet gave a classification accuracy of 88.42% and an ROC AUC of 0.9735. Model uncertainties i.e. epistemic (0.0082) and aleatoric (0.008) uncertaintieswere also calculated to provide insight into overall reliability of  the model.

There are different practical applications of this study in medical field especially medical image classification and identification.MobileNet, with ensembles, demonstrates great ability for improving accuracy of the brain tumor classification, which can make  the treatment plan possible and more effective. The accuracy of brain tumer classification based on the MRI images can reduce diagnostic errors and enhance the patient outcomes after enabling timely and precise interventions. More ever, the evaluation of the model uncertainties gives an additional layer of confidence,confirming that the predictions made by model are reliable and we can use them in real-world.

There are different practical applications of this study in medical field especially medical image classification and identification.MobileNet, with ensembles, demonstrates great ability for improving accuracy of the brain tumor classification, which can make  the treatment plan possible and more effective. The accuracy of brain tumer classification based on the MRI images can reduce diagnostic errors and enhance the patient outcomes after enabling timely and precise interventions. More ever, the evaluation of the model uncertainties gives an additional layer of confidence,confirming that the predictions made by model are reliable and we can use them in real-world.