Detecting brain tumors using deep learning convolutional neural network with transfer learning approach
Sadia Anjum
Department of IT, Hazara University 21120, KPK, Pakistan
Search for more papers by this authorCorresponding Author
Lal Hussain
Department of Computer Science & IT, Neelum Campus, The University of Azad Jammu and Kashmir, Athmuqam, Azad Kashmir, Pakistan
Department of Computer Science & IT, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, USA
Correspondence
Lal Hussain, Department of Computer Science & Information Technology, The University of Azad Jammu and Kashmir, 13100 Muzaffarabad, Azad Kashmir, Pakistan.
Email: [email protected]
Search for more papers by this authorMushtaq Ali
Department of IT, Hazara University 21120, KPK, Pakistan
Search for more papers by this authorMonagi H. Alkinani
Department of Computer Science & AI, College of Computer Science and Engineering University of Jeddah, Jeddah, Saudi Arabia
Search for more papers by this authorWajid Aziz
Department of Computer Science & IT, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
Department of Computer Science & AI, College of Computer Science and Engineering University of Jeddah, Jeddah, Saudi Arabia
Search for more papers by this authorSabrina Gheller
Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, USA
Search for more papers by this authorAdeel Ahmed Abbasi
School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
Search for more papers by this authorAli Raza Marchal
Department of Computer Science & IT, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
Search for more papers by this authorHarshini Suresh
Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, USA
Search for more papers by this authorTim Q. Duong
Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, USA
Search for more papers by this authorSadia Anjum
Department of IT, Hazara University 21120, KPK, Pakistan
Search for more papers by this authorCorresponding Author
Lal Hussain
Department of Computer Science & IT, Neelum Campus, The University of Azad Jammu and Kashmir, Athmuqam, Azad Kashmir, Pakistan
Department of Computer Science & IT, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, USA
Correspondence
Lal Hussain, Department of Computer Science & Information Technology, The University of Azad Jammu and Kashmir, 13100 Muzaffarabad, Azad Kashmir, Pakistan.
Email: [email protected]
Search for more papers by this authorMushtaq Ali
Department of IT, Hazara University 21120, KPK, Pakistan
Search for more papers by this authorMonagi H. Alkinani
Department of Computer Science & AI, College of Computer Science and Engineering University of Jeddah, Jeddah, Saudi Arabia
Search for more papers by this authorWajid Aziz
Department of Computer Science & IT, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
Department of Computer Science & AI, College of Computer Science and Engineering University of Jeddah, Jeddah, Saudi Arabia
Search for more papers by this authorSabrina Gheller
Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, USA
Search for more papers by this authorAdeel Ahmed Abbasi
School of Computer Science and Engineering, Central South University, Changsha, Hunan, China
Search for more papers by this authorAli Raza Marchal
Department of Computer Science & IT, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, Azad Kashmir, Pakistan
Search for more papers by this authorHarshini Suresh
Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, USA
Search for more papers by this authorTim Q. Duong
Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, New York, USA
Search for more papers by this authorAbstract
Accurate classification of brain tumor subtypes is important for prognosis and treatment. In this study, we optimized and applied non-deep learning methods based on hand-crafted features and deep learning methods based on transfer learning using softmax as classification and KNN and SVM as classification for features extracted from deep features of ResNet101. For non-deep learning techniques, we extracted multimodal features as input to machine learning classifiers. For convolutional neural networks, we optimized and applied GoogleNet and ResNet101with transfer learning approach. The performance was evaluated in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR), total accuracy (TA), and area under the receiver operating curve (AUC) using Jack-knife 10-fold cross validation (CV) for the testing and validation of the dataset. For two-class classification, entropy features using SVM Gaussian yielded the highest performance with 93.84% TA and 0.9874 AUC, and GoogleNet yielded 99.33% TA. For Multiclass classification, the highest performance to detect pituitary tumor yielded 95.65% accuracy and 0.95 AUC using ResNet101 with transfer learning. Deep features from ResNet101 using KNN improved detection of pituitary tumor (98.80% accuracy, 0.99 AUC), glioma (93.47% accuracy, 0.93 AUC), and meningioma (93.36% accuracy, 0.89 AUC). The deep features ResNet101-SVM to detect pituitary tumor yielded performance (98.69% accuracy, 0.98 AUC). Deep learning methods with transfer learning along with softmax and KNN and SVM as classification outperformed traditional machine learning methods. This approach may prove useful for prognosis and treatment planning to achieve better clinical outcomes.
CONFLICT OF INTEREST
The authors declare no conflicts of interest.
Open Research
DATA AVAILABILITY STATEMENT
Previously reported data were used to support this study and are available at https://github.com/chengjun583/brainTumorRetrieval. These prior studies (and datasets) are cited at relevant places within the text as references [49, 50].
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