A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning

By A. Rehman, Saeeda Naz, Muhammad Imran Razzak, F. Akram, Muhammad Imran. Circuits, systems, and signal processing, 2019

555 citations recorded by the source corpus.

Brain tumors are the most destructive disease, leading to a very short life expectancy in their highest grade. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce chance of survival of patients. The accurate diagnosis of brain tumor is a key point to make a proper treatment planning to cure and improve the existence of patients with brain tumors disease. The computer-aided tumor detection systems and convolutional neural networks provided success stories and have made important strides in the field of machine learning. The deep convolutional layers extract important and robust features automatically from the input space as compared to traditional predecessor neural network layers. In the proposed framework, we conduct three studies using three architectures of convolutional neural networks (AlexNet, GoogLeNet, and VGGNet) to classify brain tumors such as meningioma, glioma, and pituitary. Each study then explores the transfer learning techniques, i.e., fine-tune and freeze using MRI slices of brain tumor dataset—Figshare. The data augmentation techniques are applied to the MRI slices for generalization of results, increasing the dataset samples and reducing the chance of over-fitting. In the proposed studies, the fine-tune VGG16 architecture attained highest accuracy up to 98.69 in terms of classification and detection.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/b8504d447d358f48bc9e6b2d32314809794889c1

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Citations in the corpus

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network (2020, methodology): “[40] preprocessed images with contrast enhancement and augmented the dataset.”
MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques (2023, background): “Rehman et al. [22] studied three CNNs, namely AlexNet, GoogLeNet, and VGGNet.”
Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists (2020, methodology): “Other methods were also introduced in the literature for brain tumor classification, such as a generative adversarial network (GAN)-based approach [19], artificial neural network (ANN)-based learning [27], ELM-based learning [28], residual network [29], standard-features-based classification [30,31]”