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A brain tumor is a mass of abnormal cells in the brain. Brain tumors can be benign or malignant. Conventional diagnosis of a brain tumor by the radiologist, is done by examining a set of images produced by magnetic resonance imaging (MRI). Many computer-aided detection (CAD) systems have been developed in order to help the radiologist reach his goal of correctly classifying the MRI image. Convolutional neural networks (CNNs) have been widely used in the classification of medical images. This paper presents a novel CAD technique for the classification of brain tumors in MRI images The proposed system extracts features from the brain MRI images by utilizing the strong energy compactness property exhibited by the Discrete Wavelet transform (DWT). The Wavelet features are then applied to a CNN to classify the input MRI image. Experimental results indicate that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 98.5%.
Komori, T. Updating the grading criteria for adult diffuse gliomas: beyond the WHO2016CNS classification. Brain Tumor Pathol; 2020.
Koriyama S, Nitta M, Kobayashi T, et al. A surgical strategy for lower grade gliomas using intraoperative molecular diagnosis. Brain Tumor Pathol. 2018;35: 159–167.
Ogawa K, Kurose A, Kamataki A, et al. Giant cell glioblastoma is a distinctive subtype of glioma characterized by vulnerability to DNA damage. Brain Tumor Pathol. 2020;37:5–13.
Asano K, Kurose A, Kamataki A, et al. Importance and accuracy of intraoperative frozen section diagnosis of the resection margin for effective carmustine wafer implantation. Brain Tumor Pathol. 2018;35: 131–140.
Yokogami K, Yamasaki K, Matsumoto F, et al. Impact of PCR-based molecular analysis in daily diagnosis for the patient with gliomas. Brain Tumor Pathol. 2018;35:141–147.
Góes P, Santos BFO, Suzuki FS et al. Necrosis is a consistent factor to recurrence of meningiomas: should it be a stand-alone grading criterion for grade II meningioma? J Neurooncol; 2017.
Sasaki S, Tomomasa R, Nobusawa S, et al. Anaplastic pleomorphic xanthoastrocytoma associated with an H3G34 mutation: a case report with review of literature. Brain Tumor Pathol. 2019;36:169–173.
Tan CL, Vellayappan B, Wu B, et al. Molecular profiling of different glioma specimens from an Ollier disease patient suggests a multifocal disease process in the setting of IDH mosaicism. Brain Tumor Pathol. 2018;35:202–208.
Yamasaki T, Sakai N, Shinmura K, et al. Anaplastic changes of diffuse leptomeningeal glioneuronal tumor with polar spongioblastoma pattern. Brain Tumor Pathol. 2018;35:209–216.
Girolami I, Cima L, Ghimenton C, et al. NRAS mutated diffuse leptomeningeal melanomatosis in an adult patient with a brief review of the so-called “formefruste” of neurocutaneous melanosis. Brain Tumor Pathol. 2018;35:217–223.
Louis DN. A feast of reviews about brain and pituitary tumor pathology. Brain Tumor Pathol. 2018;35:49–50.
Saeed Jerban, Eric Y. Chang, Jiang Du, “Magnetic resonance imaging (MRI) studies of knee joint under mechanical loading: Review,” Magnetic Resonance Imaging. 2020;65:27-36.
Anjali Wadhwa, Anuj Bhardwaj, Vivek Singh Verma, A review on brain tumor segmentation of MRI images, Magnetic Resonance Imaging. 2019;61:247-259.
Nishioka H, Inoshita N. New WHO classification of pituitary adenomas (4th edition): Assessment of pituitary transcription factors and the prognostic histological factors. Brain Tumor Pathol. 2018;35:57–61.
Iuchi T, Sugiyama T, Ohira M, et al. Clinical significance of the 2016 WHO classification in Japanese patients with gliomas. Brain Tumor Pathol. 2018;35:71–80.
Shibuya, M. Welcoming the new WHO classification of pituitary tumors 2017: revolution in TTF-1-positive posterior pituitary tumors. Brain Tumor Pathol. 2018;35:62–70.
Akagi Y, Yoshimoto K, Hata N, et al. Reclassification of 400 consecutive glioma cases based on the revised 2016WHO classification. Brain Tumor Pathol. 2018;35:81–89.
Kuwahara K, Ohba S, Nakae S, et al. Clinical, histopathological, and molecular analyses of IDH-wild-type WHO grade II–III gliomas to establish genetic predictors of poor prognosis. Brain Tumor Pathol. 2019;36, 135–143 (2019).
Barresi V, Liont S, Caliri S, et al. Histopathological features to define atypical meningioma: What does really matter for prognosis?. Brain Tumor Pathol. 2018;35: 168–180.
Nambirajan A, Malgulwar PB, Sharma A, et al. Clinicopathological evaluation of PD-L1 expression and cytotoxic T-lymphocyte infiltrates across intracranial molecular subgroups of ependymomas: Are these tumors potential candidates for immune check-point blockade?. Brain Tumor Pathol. 2019;36:152–161.
Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K. Artificial intelligence, bias and clinical safety. BMJ QualSaf. 2019;28:231–37.3.
Crawford K, Calo R. There is a blind spot in AI research. Nature. 2016;538:311–13.4.
Lallas A, Argenziano G. Artificial intelligence and melanoma diagnosis: ignoring human nature may lead to false predictions. Dermatol Pract Concept. 2018; 8:249–51.5
Tiwari A, Srivastava S, Pant M. Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognition Letters; 2019.
hakeel PM, Tobely TEEL, Al-feel H, Manogaran G, Baskar S. Neural network based brain tumor detection using wireless infrared imagingsensor. In IEEE Access. 2019;7:5577–5588.
Maalinii GB, Jatti A. Brain tumour extraction using morphological reconstruction and thresholding,” Materials Today Proceedings. 2018;5(4):10689–10696.
Jemimma TA, Vetharaj YJ. Watershed algorithm based DAPP features for brain tumor segmentation and classification. In 2018 International Conference on Smart Systems and Inventive Technology. 2018;155158.
Yin B, Wang C, Abza F. New brain tumor classification method based on an improved version of whale optimization algorithm. Biomedical Signal Processing and Control. 2019;56:101728.
Gurbină M, Lascu M, Lascu D. Tumor detection and classification of MRI brain image using different wavelet transforms and support vector machines. 2019;505–508.
Shree NV. Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brian Informatics. 2018;5(1):23–30.
Stachera M, Sznajder K. Magnetic resonance in human brain examinations: A brief outline of the techniques. Utrecht: Cornell University; 2014.
Ahmad M. Sarhan, Radaan Al-Dosari, Mammogram classification using discrete wavelet transform features and a novel vector quantization technique for breast cancer detection, British Journal of Applied Science & Technology. 2017;19(1).
Ahmad M. Sarhan. A WPD scanning technique for iris recognition, International Journal of Computer Applications. 2014;85(14):6-12.
Ahmad M. Sarhan. Wavelet-based feature extraction for DNA microarray classification, Artificial Intelligence Review (Springer). 2013;39(3):237-249.
Khalid A. Buragga, Sultan Aljahdali, Sarhan AM. An Efficient Technique for Iris Recognition using Wavelets and Artificial Neural Networks," In Proceedings of CATA 2015, Hawaii, USA; 2015.
Ahmad M. Sarhan. A comparison of vector quantization and artificial neural network techniques in typed Arabic character recognition," International Journal of Applied Engineering Research (IJAER). 2009;4(5):805-817.
Sarhan AM. Optimal statistical artificial neural networks for Arabic character recognition," In Proceedings of 16th Int'l Conference on Computers and Their Applications, Cancun, Mexico. 2008;53-58.
Sarhan AM, Al-Helalat OI. Probabilistic artificial neural networks for Arabic character recognition, In Proceedings of 16th Int'l Conference on Software Engineering and Data Engineering, Las Vegas; 2007.
Sarhan AM, Al-Helalat OI. Arabic character recognition using artificial neural networks and statistical analysis," In Proceedings of the ICCESSE Conference. 2007;32-36.
Huang G, Liu Z, Pleiss G, Van Der Maaten L, Weinberger K. Convolutional Networks with Dense Connectivity, IEEE Trans. Pattern Anal. and Mach. Intell; 2019.
Han X, Zhong Y, Cao L, Zhang L. Pre-trained AlexNet architecture with pyramid pooling and supervision for high spatial resolution remote sensing image scene classification. Remote Sens. 2017;9: 848.
Cortes C, Vapnik V. Support vector networks," Machine Learning. 1995;20: 273-297,
Liu X, Faes L, Kale A, et al. A comparison of deep learning performance against health care profesisonals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digital Health; 2019.
Segera, Davies, Mbuthia, Mwangi, Nyete, Abraham. TI - Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis,” BioMed Research International. 2019.
Ahmad M. Sarhan. A novel gene-based cancer diagnosis with wavelets and support vector machines, European Journal of Scientific Research (EJSR). 2010;46(4): 488-502.
Wang L. (Ed.). Support vector machines: Theory and applications vector machines. Computer Science. 2005;177: 6221.