A Hybrid Deep Learning-Based Approach for Brain Tumor Classification
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/7/1146 |
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| author | Asaf Raza Huma Ayub Javed Ali Khan Ijaz Ahmad Ahmed S. Salama Yousef Ibrahim Daradkeh Danish Javeed Ateeq Ur Rehman Habib Hamam |
| author_facet | Asaf Raza Huma Ayub Javed Ali Khan Ijaz Ahmad Ahmed S. Salama Yousef Ibrahim Daradkeh Danish Javeed Ateeq Ur Rehman Habib Hamam |
| author_sort | Asaf Raza |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:56:55Z |
| format | Article |
| id | doaj.art-ca424feb48524a2db6c0f1fbe7f365e6 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T11:56:55Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-ca424feb48524a2db6c0f1fbe7f365e62023-11-30T23:08:05ZengMDPI AGElectronics2079-92922022-04-01117114610.3390/electronics11071146A Hybrid Deep Learning-Based Approach for Brain Tumor ClassificationAsaf Raza0Huma Ayub1Javed Ali Khan2Ijaz Ahmad3Ahmed S. Salama4Yousef Ibrahim Daradkeh5Danish Javeed6Ateeq Ur Rehman7Habib Hamam8Department of Software Engineering, University of Engineering and Technology, Taxila 44000, PakistanDepartment of Software Engineering, University of Engineering and Technology, Taxila 44000, PakistanDepartment of Software Engineering, University of Science and Technology Bannu, Bannu 28100, PakistanShenzhen College of Advanced Technology, University of Chinese Academy of Sciences (UCAS), Shenzhen 518055, ChinaElectrical Engineering Department, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, EgyptDepartment of Computer Engineering and Networks, College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addawasir 11991, Saudi ArabiaSoftware College, Northeastern University, Shenyang 110169, ChinaDepartment of Electrical Engineering, Government College University, Lahore 54000, PakistanFaculty of Engineering, Uni de Moncton, Moncton, NB E1A3E9, CanadaBrain 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.https://www.mdpi.com/2079-9292/11/7/1146deep learningbrain tumorMRItransfer learningconvolutional neural network |
| spellingShingle | Asaf Raza Huma Ayub Javed Ali Khan Ijaz Ahmad Ahmed S. Salama Yousef Ibrahim Daradkeh Danish Javeed Ateeq Ur Rehman Habib Hamam A Hybrid Deep Learning-Based Approach for Brain Tumor Classification Electronics deep learning brain tumor MRI transfer learning convolutional neural network |
| title | A Hybrid Deep Learning-Based Approach for Brain Tumor Classification |
| title_full | A Hybrid Deep Learning-Based Approach for Brain Tumor Classification |
| title_fullStr | A Hybrid Deep Learning-Based Approach for Brain Tumor Classification |
| title_full_unstemmed | A Hybrid Deep Learning-Based Approach for Brain Tumor Classification |
| title_short | A Hybrid Deep Learning-Based Approach for Brain Tumor Classification |
| title_sort | hybrid deep learning based approach for brain tumor classification |
| topic | deep learning brain tumor MRI transfer learning convolutional neural network |
| url | https://www.mdpi.com/2079-9292/11/7/1146 |
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