Isolated Convolutional-Neural-Network-Based Deep-Feature Extraction for Brain Tumor Classification Using Shallow Classifier
Yassir Edrees Almalki,
Muhammad Umair Ali,
Karam Dad Kallu,
Manzar Masud,
Amad Zafar,
Sharifa Khalid Alduraibi,
Muhammad Irfan,
Mohammad Abd Alkhalik Basha,
Hassan A. Alshamrani,
Alaa Khalid Alduraibi,
Mervat Aboualkheir
Affiliations
Yassir Edrees Almalki
Division of Radiology, Department of Internal Medicine, Medical College, Najran University, Najran 61441, Saudi Arabia
Muhammad Umair Ali
Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Korea
Karam Dad Kallu
Department of Robotics and Intelligent Machine Engineering (RIME), School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST) H−12, Islamabad 44000, Pakistan
Manzar Masud
Department of Mechanical Engineering, Capital University of Science and Technology (CUST), Islamabad 44000, Pakistan
Amad Zafar
Department of Electrical Engineering, The Ibadat International University, Islamabad 54590, Pakistan
Sharifa Khalid Alduraibi
Department of Radiology, College of Medicine, Qassim University, Buraidah 52571, Saudi Arabia
Muhammad Irfan
Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia
Mohammad Abd Alkhalik Basha
Radiology Department, Faculty of Human Medicine, Zagazig University, Zagazig 44631, Egypt
Hassan A. Alshamrani
Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia
Alaa Khalid Alduraibi
Department of Radiology, College of Medicine, Qassim University, Buraidah 52571, Saudi Arabia
Mervat Aboualkheir
Department of Radiology and Medical Imaging, College of Medicine, Taibah University, Madinah 42353, Saudi Arabia
In today’s world, a brain tumor is one of the most serious diseases. If it is detected at an advanced stage, it might lead to a very limited survival rate. Therefore, brain tumor classification is crucial for appropriate therapeutic planning to improve patient life quality. This research investigates a deep-feature-trained brain tumor detection and differentiation model using classical/linear machine learning classifiers (MLCs). In this study, transfer learning is used to obtain deep brain magnetic resonance imaging (MRI) scan features from a constructed convolutional neural network (CNN). First, multiple layers (19, 22, and 25) of isolated CNNs are constructed and trained to evaluate the performance. The developed CNN models are then utilized for training the multiple MLCs by extracting deep features via transfer learning. The available brain MRI datasets are employed to validate the proposed approach. The deep features of pre-trained models are also extracted to evaluate and compare their performance with the proposed approach. The proposed CNN deep-feature-trained support vector machine model yielded higher accuracy than other commonly used pre-trained deep-feature MLC training models. The presented approach detects and distinguishes brain tumors with 98% accuracy. It also has a good classification rate (97.2%) for an unknown dataset not used to train the model. Following extensive testing and analysis, the suggested technique might be helpful in assisting doctors in diagnosing brain tumors.
