Brain Tumor Detection Based on Deep Features Concatenation and Machine Learning Classifiers With Genetic Selection

The development of brain tumors is often a result of cellular abnormalities, making it a leading factor contributing to mortality among both adults and children on a global scale. However, early detection of tumor can potentially prevent millions of deaths. In this regard, Magnetic Resonance Imaging (MRI) has become a pivotal tool for early brain tumor detection, It holds a vital significance role in enhancing tumor visibility that facilitates subsequent treatment planning and intervention. This research focuses on early stage brain tumor detection, proposing a Computer-Aided Detection (CAD) system that leverages MRI. Utilizing transfer learning, multiple pre-trained deep convolutional neural networks namely VGG-16, Inception V3, ResNet-101, and DenseNet- 201 are used to extract deep features from brain MRI images. Subsequently, the extracted deep features are concatenated and subjected to a genetic algorithm, acting as a technique for feature selection to determine the most important features. These features undergo evaluation using various machine learning classifiers. Two open-access brain MRI datasets, Navoneel brain tumor and Br35H Brain Tumor Detection datasets, are employed to assess model performance. Multiple experiments were conducted using the two datasets: one without feature concatenation or selection, and the other with both processes applied. The experimental results demonstrate that combining and selecting deep features leads to a substantial performance improvement, achieving an accuracy of 99.7% and 99.8% for the first and the second datasets, respectively, that surpasses the other methods.