A Hybrid Deep Learning-Based Approach for Brain Tumor Classification

Electronics

Yousef Ibrahim Daradkeh¹  Ateeq Ur Rehman²  Asaf Raza³  Huma Ayub³  Javed Ali Khan⁴  Ahmed S. Salama⁵  Habib Hamam⁶  Ijaz Ahmad⁷  Danish Javeed⁸ 

[1] Department of Computer Engineering and Networks, College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addawasir 11991, Saudi Arabia;Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan;Department of Software Engineering, University of Engineering and Technology, Taxila 44000, Pakistan;Department of Software Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan;Electrical Engineering Department, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt;Faculty of Engineering, Uni de Moncton, Moncton, NB E1A3E9, Canada;Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences (UCAS), Shenzhen 518055, China;Software College, Northeastern University, Shenyang 110169, China;

关键词: deep learning;    brain tumor;    MRI;    transfer learning;    convolutional neural network;   

DOI  :  10.3390/electronics11071146

来源: DOAJ

【 摘 要 】

Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of people die due to deadly brain tumors. Therefore, accurate detection and classification are essential in the treatment of brain tumors. Numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand-crafted features, which is very time-consuming. On the contrary, DL is very robust in feature extraction and has recently been widely used for classification and detection purposes. Therefore, in this work, we propose a hybrid deep learning model called DeepTumorNet for three types of brain tumors (BTs)—glioma, meningioma, and pituitary tumor classification—by adopting a basic convolutional neural network (CNN) architecture. The GoogLeNet architecture of the CNN model was used as a base. While developing the hybrid DeepTumorNet approach, the last 5 layers of GoogLeNet were removed, and 15 new layers were added instead of these 5 layers. Furthermore, we also utilized a leaky ReLU activation function in the feature map to increase the expressiveness of the model. The proposed model was tested on a publicly available research dataset for evaluation purposes, and it obtained 99.67% accuracy, 99.6% precision, 100% recall, and a 99.66% F1-score. The proposed methodology obtained the highest accuracy compared with the state-of-the-art classification results obtained with Alex net, Resnet50, darknet53, Shufflenet, GoogLeNet, SqueezeNet, ResNet101, Exception Net, and MobileNetv2. The proposed model showed its superiority over the existing models for BT classification from the MRI images.

【 授权许可】

Unknown