Efficient Brain Tumor Classification through Transfer Learning Models

One of the most dangerous diseases, brain tumors need a quick and accurate method of detection. Wide-ranging symptoms can be caused by brain tumors, and both diagnosing and treating them can be difficult. Neurologists and radiologists occasionally fail to find the brain tumor. The American Brain Tumor Association says, about 700,000 people are coping with brain tumors. Each year, more than 80,000 new cases are diagnosed. The goal of this work is to identify brain tumors from MRI images in order to prevent earlier death. Brain image classification is done using the DenseNet121 and EfficientNetB0 pretrained transfer learning model. DenseNet121 and EfficientNetB0 have been selected for brain tumor classification because of their robust performance in image analysis tasks, their pretrained knowledge from extensive datasets like ImageNet, and their computational efficiency. The experimental result shows that the proposed model performs better than the previous work model VGG16, Support Vector Machine (SVM), Discrete Wavelet Transform (DWT), Gray Level Co-occurrence Matrix(GLCM), Hidden Markov Model (HMM), and Convolution Neural Network (CNN). These previous models’ classification accuracy ranges from 94% to 96%, while the suggested model where we used 80% data for training and 20% data used for testing and average accuracies are 97.09% for DenseNet121 and 98.32% for EfficientNetB0. Also, the purpose of this work to categorize 4 classes in these 3 classes for tumor and 1 class for non tumor. Additionally, the proposed model’s precision, recall, and F1-score are all 97% and 98%, indicating very good performance. However, these techniques have shown a significant potential for improving the diagnosis and treatment of brain tumors by providing a quick, accurate, and non-invasive way to categorize different types of tumors.