A review on detecting brain tumors using deep learning and magnetic resonance images

Abstract

Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained.

Image classification-based transfer learning framework for image detection of IoT devices

“…The works in [8]- [11] proposes TL frameworks using pre-trained convolutional neural networks (CNN) models for medical images classifications including melanoma detection, anthracnose and red-rust leaf disease detection, diabetic retinopathy identification, and pneumonia classification. Meanwhile, some CNNbased TL approaches for brain and breast tumor detection using magnetic resonance images have been investigated in [12]- [14]. In [15]- [18], CNN-based TL methods are implemented for COVID-19 detection using chest X-ray images.…”