An Effective Approach to Detect and Identify Brain Tumors Using Transfer Learning

Applied Sciences

Marwa Obayya¹  Noha Negm²  Mohammad Sohail Khan³  Ahmed S. Salama⁴  Wahab Khan⁵  Naeem Ullah⁶  Javed Ali Khan⁷  Izaz Hassan⁷ 

[1] Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha 62529, Saudi Arabia;Department of Computer Software Engineering, University of Engineering and Technology Mardan, Mardan 23200, Pakistan;Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt;Department of Electrical Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan;Department of Software Engineering, University of Engineering and Technology, Taxila 47050, Pakistan;Department of Software Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan;

关键词: brain tumor;    deep learning;    inceptionResNetv2;    transfer learning;    tumor detection;   

DOI  :  10.3390/app12115645

来源: DOAJ

【 摘 要 】

Brain tumors are considered one of the most serious, prominent and life-threatening diseases globally. Brain tumors cause thousands of deaths every year around the globe because of the rapid growth of tumor cells. Therefore, timely analysis and automatic detection of brain tumors are required to save the lives of thousands of people around the globe. Recently, deep transfer learning (TL) approaches are most widely used to detect and classify the three most prominent types of brain tumors, i.e., glioma, meningioma and pituitary. For this purpose, we employ state-of-the-art pre-trained TL techniques to identify and detect glioma, meningioma and pituitary brain tumors. The aim is to identify the performance of nine pre-trained TL classifiers, i.e., Inceptionresnetv2, Inceptionv3, Xception, Resnet18, Resnet50, Resnet101, Shufflenet, Densenet201 and Mobilenetv2, by automatically identifying and detecting brain tumors using a fine-grained classification approach. For this, the TL algorithms are evaluated on a baseline brain tumor classification (MRI) dataset, which is freely available on Kaggle. Additionally, all deep learning (DL) models are fine-tuned with their default values. The fine-grained classification experiment demonstrates that the inceptionresnetv2 TL algorithm performs better and achieves the highest accuracy in detecting and classifying glioma, meningioma and pituitary brain tumors, and hence it can be classified as the best classification algorithm. We achieve 98.91% accuracy, 98.28% precision, 99.75% recall and 99% F-measure values with the inceptionresnetv2 TL algorithm, which out-performs the other DL algorithms. Additionally, to ensure and validate the performance of TL classifiers, we compare the efficacy of the inceptionresnetv2 TL algorithm with hybrid approaches, in which we use convolutional neural networks (CNN) for deep feature extraction and a Support Vector Machine (SVM) for classification. Similarly, the experiment’s results show that TL algorithms, and inceptionresnetv2 in particular, out-perform the state-of-the-art DL algorithms in classifying brain MRI images into glioma, meningioma, and pituitary. The hybrid DL approaches used in the experiments are Mobilnetv2, Densenet201, Squeeznet, Alexnet, Googlenet, Inceptionv3, Resnet50, Resnet18, Resnet101, Xception, Inceptionresnetv3, VGG19 and Shufflenet.

【 授权许可】

Unknown