Advanced Deep Learning Approaches for Accurate Brain Tumor Classification in Medical Imaging

Abstract

A brain tumor can have an impact on the symmetry of a person’s face or head, depending on its location and size. If a brain tumor is located in an area that affects the muscles responsible for facial symmetry, it can cause asymmetry. However, not all brain tumors cause asymmetry. Some tumors may be located in areas that do not affect facial symmetry or head shape. Additionally, the asymmetry caused by a brain tumor may be subtle and not easily noticeable, especially in the early stages of the condition. Brain tumor classification using deep learning involves using artificial neural networks to analyze medical images of the brain and classify them as either benign (not cancerous) or malignant (cancerous). In the field of medical imaging, Convolutional Neural Networks (CNN) have been used for tasks such as the classification of brain tumors. These models can then be used to assist in the diagnosis of brain tumors in new cases. Brain tissues can be analyzed using magnetic resonance imaging (MRI). By misdiagnosing forms of brain tumors, patients’ chances of survival will be significantly lowered. Checking the patient’s MRI scans is a common way to detect existing brain tumors. This approach takes a long time and is prone to human mistakes when dealing with large amounts of data and various kinds of brain tumors. In our proposed research, Convolutional Neural Network (CNN) models were trained to detect the three most prevalent forms of brain tumors, i.e., Glioma, Meningioma, and Pituitary; they were optimized using Aquila Optimizer (AQO), which was used for the initial population generation and modification for the selected dataset, dividing it into 80% for the training set and 20% for the testing set. We used the VGG-16, VGG-19, and Inception-V3 architectures with AQO optimizer for the training and validation of the brain tumor dataset and to obtain the best accuracy of 98.95% for the VGG-19 model.

A Robust Hybrid Convolutional Network for Tumor Classification Using Brain MRI Image Datasets

“…Authors proposed a CNN model such as VGG-(16 and 19) and InceptionV3 with Aquila optimizer. They classified brain tumor detection with an accuracy of 98.95% for the VGG-19 but testing accuracy is not given on the test dataset for this VGG-19 model [11]. Other authors proposed a CNN model with a ResNet50 architecture for the detection of brain cancers and found a correctness percentage of 98% in the MRI dataset [12].…”

Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification

“…Mahmoud et al trained CNN models for detecting the most prevalent brain tumor types and achieved an impressive accuracy of 98.95%, particularly with the VGG-19 model [37]. Diaz-Pernas et al presented a BTC model using a multiscale CNN.…”

Deep learning-based automated scan plane positioning for brain magnetic resonance imaging

“…Consequently, the robustness and accuracy of automated scan plane positioning algorithms must account for individual patient variations. The automated scan plane positioning algorithms must be able to recognize and adapt to anatomical variations in different patients to ensure accuracy and consistency of localization ( 9 ). Finally, even the same anatomical region may have a variety of different application needs for localization in the clinic.…”