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A study of classification and feature extraction techniques for brain tumor detection

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Abstract

Medical imaging aids in the analysis of interior parts of the human body such as the functioning of the organs or tissues for early treatment of diseases. Many different types of medical imaging technologies exist, for example, X-ray radiography, magnetic resonance imaging, endoscopy, positron emission tomography, CT scan (computed tomography), and many more. A tumor is an abnormal tissue in the brain which causes damage to the functioning of the cell. Therefore, brain tumor detection is an incredibly tricky task. Manual detection of a tumor is quite risky as it involves the insertion of a needle in the brain. Thus, there is a need for automated brain tumor detection systems. The well-timed detection of the tumor can add to accurate treatment and can increase the survival rate of patients. From machine learning techniques, namely K-nearest neighbor, support vector machine, and more to soft computing techniques, namely artificial neural network, self-organizing map, and others hold a significant stand in detection and categorization of brain tumor. Various methods including deep learning-based classifiers such as convolutional neural network, recurrent neural network, deep belief network (DBN), and others are used to make it easier to detect the tumor. Hybrid classifiers were also used for classification systems such as combining the machine learning approach with soft computing. This study is to summarize and compare the work of various authors on automatic brain tumor detection using medical imaging. Based on the accuracy, specificity, and sensitivity parameters, the results of different techniques are analyzed and compared graphically.

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References

  1. Narasimhamurthy A (2017) An overview of machine learning in medical image analysis. Med Imaging. https://doi.org/10.4018/978-1-5225-0571-6.ch002

    Article  Google Scholar 

  2. Tyagi V (2018) Introduction to digital image processing. Underst Digit Image Process. https://doi.org/10.1201/9781315123905-1

    Article  Google Scholar 

  3. Silva EA, Mendonça GV (2005) Digital image processing. In: Dorf RC (ed) The electrical engineering handbook. CRC Press, Boca Raton, pp 891–910. https://doi.org/10.1016/b978-012170960-0/50064-5

    Chapter  Google Scholar 

  4. Gan WS (2020) Digital image processing. Signal Process Image Process Acoust Imaging. https://doi.org/10.1007/978-981-10-5550-8_10

    Article  Google Scholar 

  5. Arora A (2019) Fundamental steps of digital image processing. https://medium.com/futframe-ai/fundamental-steps-of-digital-image-processing-d7518d6bb23c

  6. Kissane J, Neutze JA, Singh H (2020) MRI. In: Kissane J, Neutze JA, Singh H (eds) Radiology fundamentals. Springer, Berlin, pp 33–35. https://doi.org/10.1007/978-3-030-22173-7_7

    Chapter  Google Scholar 

  7. Hardan H (2016) Image processing—Philadelphia University. https://www.philadelphia.edu.jo/academics/hhardan/uploads/Image_Processing-ch1_part_1.pdf

  8. Venkat E (2016) Digital image processing—lecture notes. https://www.slideshare.net/ezhilyavenkat/digital-image-processing-lecture-notes

  9. Kurka PR, Salazar AA (2019) Applications of image processing in robotics and instrumentation. Mech Syst Signal Process 124:142–169. https://doi.org/10.1016/j.ymssp.2019.01.015

    Article  Google Scholar 

  10. Sert E, Özyurt F, Doğantekin A (2019) A new approach for brain tumor diagnosis system: single image super resolution based maximum fuzzy entropy segmentation and convolutional neural network. Med Hypotheses 133:109413. https://doi.org/10.1016/j.mehy.2019.109413

    Article  Google Scholar 

  11. Seeram E (2020) Digital image processing concepts. Digit Radiogr. https://doi.org/10.1007/978-981-15-6522-9_2

    Article  Google Scholar 

  12. Du E, Ives R, Nevel AV, She J (2011) Advanced image processing for defense and security applications. EURASIP J Adv Signal Process. https://doi.org/10.1155/2010/432972

    Article  Google Scholar 

  13. Sun Z, Ng K, Ramli N (2011) Biomedical imaging research: a fast-emerging area for interdisciplinary collaboration. https://www.ncbi.nlm.nih.gov/pubmed/22279498

  14. Harif M, Stefan DC (2017) Early warning signs and diagnostic approach in childhood cancer. Pediatr Cancer Afr. https://doi.org/10.1007/978-3-319-17936-0_2

    Article  Google Scholar 

  15. Deserno TM (2010) Fundamentals of biomedical image processing. In: Deserno T (ed) Biomedical image processing biological and medical physics, biomedical engineering. Springer, Berlin, pp 1–51. https://doi.org/10.1007/978-3-642-15816-2_1

    Chapter  Google Scholar 

  16. Banan R, Hartmann C (2017) The new WHO 2016 classification of brain tumors-what neurosurgeons need to know. Retrieved October 10, 2020, from https://pubmed.ncbi.nlm.nih.gov/28093610/

  17. Zimmerman RA, Bilaniuk LT (2000) Brain tumors. Neuroimaging. https://doi.org/10.1007/978-1-4612-1152-5_27

    Article  Google Scholar 

  18. Leece R, Xu J, Ostrom QT, Chen Y, Kruchko C, Barnholtz-Sloan JS (2017) Global incidence of malignant brain and other central nervous system tumors by histology, 2003–2007. Neuro-Oncology 19(11):1553–1564. https://doi.org/10.1093/neuonc/nox09

    Article  Google Scholar 

  19. Alentorn A, Hoang-Xuan K, Mikkelsen T (2016) Presenting signs and symptoms in brain tumors. In: Berger MS, Weller M (eds) handbook of clinical neurology gliomas. Elsevier, Amsterdam, pp 19–26. https://doi.org/10.1016/b978-0-12-802997-8.00002-5

    Chapter  Google Scholar 

  20. Abd-Ellah MK, Awad AI, Khalaf AA, Hamed HF (2019) A review on brain tumor diagnosis from MRI images: practical implications, key achievements, and lessons learned. Magn Reson Imaging 61:300–318. https://doi.org/10.1016/j.mri.2019.05.028

    Article  Google Scholar 

  21. Eckenstein M, Thomas AA (2020) Benign and malignant tumors of the central nervous system and pregnancy. In: Steegers EAP, Cipolla MJ, Miller EC (eds) Handbook of clinical neurology and pregnancy: neuro-obstetric disorders. Elsevier, Amsterdam, pp 241–258. https://doi.org/10.1016/b978-0-444-64240-0.00014-3

    Chapter  Google Scholar 

  22. Ata ES, Turgut M, Eraslan C, Dayanır YÖ (2016) Comparison between dynamic susceptibility contrast magnetic resonance imaging and arterial spin labeling techniques in distinguishing malignant from benign brain tumors. Eur J Radiol 85(9):1545–1553. https://doi.org/10.1016/j.ejrad.2016.05.015

    Article  Google Scholar 

  23. Spine M (2018) Brain biopsy. https://mayfieldclinic.com/pe-brainbiopsy.htm

  24. Babu AE, Subhash A, Rajan D, Jacob F, Kumar PA (2018) A survey on methods for brain tumor detection. In: 2018 conference on emerging devices and smart systems (ICEDSS). https://doi.org/10.1109/icedss.2018.8544353

  25. Mehekare V (2017) Brain tumor detection using neural network. Int J Adv Res Electr Electron Instrum Eng. https://doi.org/10.15662/IJAREEIE.2017.0605082

    Article  Google Scholar 

  26. Lahmiri S (2017) Glioma detection based on multi-fractal features of segmented brain MRI by particle swarm optimization techniques. Biomed Signal Process Control 31:148–155. https://doi.org/10.1016/j.bspc.2016.07.008

    Article  Google Scholar 

  27. Devi N, Bhattacharyya K (2018) automatic brain tumor detection and classification of grades of astrocytoma. In: Proceedings of the international conference on computing and communication systems lecture notes in networks and systems, pp 125–135. https://doi.org/10.1007/978-981-10-6890-4_11

  28. Anjali R, Priya S (2017) An efficient classifier for brain tumor classification. https://www.ijcsmc.com/docs/papers/August2017/V6I8201711.pdf

  29. Chander PS, Soundarya J, Priyadharsini R (2019) Brain tumour detection and classification using K-means clustering and SVM classifier. In: Abdul Majeed PP, Mat-Jizat J, Hassan M, Taha Z, Choi H, Kim J (eds) Lecture notes in mechanical engineering RITA 2018. Springer, Singapore, pp 49–63. https://doi.org/10.1007/978-981-13-8323-6_5

    Chapter  Google Scholar 

  30. Rajan PG, Sundar C (2019) Brain tumor detection and segmentation by intensity adjustment. J Med Syst. https://doi.org/10.1007/s10916-019-1368-4

    Article  Google Scholar 

  31. Vallabhaneni RB, Rajesh V (2018) Brain tumour detection using mean shift clustering and GLCM features with edge adaptive total variation denoising technique. Alex Eng J 57(4):2387–2392. https://doi.org/10.1016/j.aej.2017.09.011

    Article  Google Scholar 

  32. Devkota B, Alsadoon A, Prasad P, Singh A, Elchouemi A (2018) Image segmentation for early stage brain tumor detection using mathematical morphological reconstruction. Procedia Comput Sci 125:115–123. https://doi.org/10.1016/j.procs.2017.12.017

    Article  Google Scholar 

  33. Kumar A, Ashok A, Ansari MA (2018) Brain tumor classification using hybrid model of PSO and SVM classifier. In: 2018 international conference on advances in computing, communication control and networking (ICACCCN). https://doi.org/10.1109/icacccn.2018.8748787

  34. Song G, Huang Z, Zhao Y, Zhao X, Liu Y, Bao M et al (2019) A NONINVASIVE system for the automatic detection of gliomas based on hybrid features and PSO-KSVM. IEEE Access 7:13842–13855. https://doi.org/10.1109/access.2019.2894435

    Article  Google Scholar 

  35. Kaplan K, Kaya Y, Kuncan M, Ertunç HM (2020) Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Med Hypotheses 139:109696. https://doi.org/10.1016/j.mehy.2020.109696

    Article  Google Scholar 

  36. Abbasi S, Tajeripour F (2017) Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 219:526–535. https://doi.org/10.1016/j.neucom.2016.09.051

    Article  Google Scholar 

  37. Amin J, Sharif M, Raza M, Saba T, Anjum MA (2019) Brain tumor detection using statistical and machine learning method. Comput Methods Programs Biomed 177:69–79. https://doi.org/10.1016/j.cmpb.2019.05.015

    Article  Google Scholar 

  38. Hargrave M (2020) How deep learning can help prevent financial fraud. https://www.investopedia.com/terms/d/deep-learning.asp

  39. Afshar P, Plataniotis KN, Mohammadi A (2019) Capsule networks for brain tumor classification based on MRI images and coarse tumor boundaries. In: ICASSP 2019—2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). https://doi.org/10.1109/icassp.2019.8683759

  40. Maharjan S, Alsadoon A, Prasad P, Al-Dalain T, Alsadoon OH (2020) A novel enhanced softmax loss function for brain tumour detection using deep learning. J Neurosci Methods 330:108520. https://doi.org/10.1016/j.jneumeth.2019.108520

    Article  Google Scholar 

  41. Das S, Aranya OR, Labiba NN (2019) Brain tumor classification using convolutional neural network. In: 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT). https://doi.org/10.1109/icasert.2019.8934603

  42. Kumar S, Mankame DP (2020) Optimization driven deep convolution neural network for brain tumor classification. Biocybern Biomed Eng 40(3):1190–1204. https://doi.org/10.1016/j.bbe.2020.05.009

    Article  Google Scholar 

  43. Ghahfarrokhi SS, Khodadadi H (2020) Human brain tumor diagnosis using the combination of the complexity measures and texture features through magnetic resonance image. Biomed Signal Process Control 61:102025. https://doi.org/10.1016/j.bspc.2020.102025

    Article  Google Scholar 

  44. Saba T, Mohamed AS, El-Affendi M, Amin J, Sharif M (2020) Brain tumor detection using fusion of hand crafted and deep learning features. Cogn Syst Res 59:221–230. https://doi.org/10.1016/j.cogsys.2019.09.007

    Article  Google Scholar 

  45. Garg V, Bansal M, Sanjana A, Dave M (2020) Analysis and detection of brain tumor using U-net-based deep learning. In: Arai K, Kapoor S, Bhatia R (eds) Advances in intelligent systems and computing intelligent computing. Springer, Cham, pp 161–173. https://doi.org/10.1007/978-3-030-52243-8_13

    Chapter  Google Scholar 

  46. Amin J, Sharif M, Yasmin M, Fernandes SL (2018) Big data analysis for brain tumor detection: deep convolutional neural networks. Future Gener Comput Syst 87:290–297. https://doi.org/10.1016/j.future.2018.04.065

    Article  Google Scholar 

  47. Zhang D, Huang G, Zhang Q, Han J, Han J, Yu Y (2020) Cross-modality deep feature learning for brain tumor segmentation. Pattern Recogn. https://doi.org/10.1016/j.patcog.2020.107562

    Article  Google Scholar 

  48. Dong H, Yang G, Liu F, Mo Y, Guo Y (2017) Automatic brain tumor detection and segmentation using U-net based fully convolutional networks. In: Valdés HM, González-Castro V (eds) Communications in computer and information science medical image understanding and analysis. Springer, Cham, pp 506–517. https://doi.org/10.1007/978-3-319-60964-5_44

    Chapter  Google Scholar 

  49. Naser MA, Deen MJ (2020) Brain tumor segmentation and grading of lower-grade glioma using deep learning in MRI images. Comput Biol Med 121:103758. https://doi.org/10.1016/j.compbiomed.2020.103758

    Article  Google Scholar 

  50. Özyurt F, Sert E, Avci E, Dogantekin E (2019) Brain tumor detection based on convolutional neural network with neutrosophic expert maximum fuzzy sure entropy. Measurement 147:106830. https://doi.org/10.1016/j.measurement.2019.07.058

    Article  Google Scholar 

  51. Parveen, Singh A (2016) Detection of brain tumor in MRI images, using fuzzy C-means segmented images and artificial neural network. In: Proceedings of the international conference on recent cognizance in wireless communication and image processing, pp 123–131. https://doi.org/10.1007/978-81-322-2638-3_14

  52. Selvapandian A, Manivannan K (2018) Fusion based Glioma brain tumor detection and segmentation using ANFIS classification. Comput Methods Programs Biomed 166:33–38. https://doi.org/10.1016/j.cmpb.2018.09.006

    Article  Google Scholar 

  53. Vijay V, Kavitha A, Rebecca SR (2016) Automated brain tumor segmentation and detection in MRI using enhanced darwinian particle swarm optimization (EDPSO). Procedia Comput Sci 92:475–480. https://doi.org/10.1016/j.procs.2016.07.370

    Article  Google Scholar 

  54. Shakeel PM, Tobely TE, Al-Feel H, Manogaran G, Baskar S (2019) Neural network based brain tumor detection using wireless infrared imaging sensor. IEEE Access 7:5577–5588. https://doi.org/10.1109/access.2018.2883957

    Article  Google Scholar 

  55. Raju AR, Suresh P, Rao RR (2018) Bayesian HCS-based multi-SVNN: a classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering. Biocybern Biomed Eng 38(3):646–660. https://doi.org/10.1016/j.bbe.2018.05.001

    Article  Google Scholar 

  56. Hashemzehi R, Mahdavi SJ, Kheirabadi M, Kamel SR (2020) Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE. Biocybern Biomed Eng 40(3):1225–1232. https://doi.org/10.1016/j.bbe.2020.06.001

    Article  Google Scholar 

  57. Iftekharuddin KM, Zheng J, Islam MA, Ogg RJ (2009) Fractal-based brain tumor detection in multimodal MRI. Appl Math Comput 207(1):23–41. https://doi.org/10.1016/j.amc.2007.10.063

    Article  MathSciNet  MATH  Google Scholar 

  58. Sharma M, Purohit GN, Mukherjee S (2017) information retrieves from brain mri images for tumor detection using hybrid technique K-means and artificial neural network (KMANN). In: Perez G, Mishra K, Tiwari S, Trivedi M (eds) Networking communication and data knowledge engineering lecture notes on data engineering and communications technologies. Springer, Singapore, pp 145–157

    Google Scholar 

  59. Minz A, Mahobiya C (2017) MR image classification using adaboost for brain tumor type. In: 2017 IEEE 7th international advance computing conference (IACC).https://doi.org/10.1109/iacc.2017.0146

  60. Amin J, Sharif M, Yasmin M, Fernandes SL (2017) A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn Lett. https://doi.org/10.1016/j.patrec.2017.10.036

    Article  Google Scholar 

  61. Cheng Y, Qin G, Zhao R, Liang Y, Sun M (2019) ConvCaps: multi-input capsule network for brain tumor classification. In: Gedeon T, Wong K, Lee M (eds) Neural information processing lecture notes in computer science. Springer, Cham, pp 524–534. https://doi.org/10.1007/978-3-030-36708-4_43

    Chapter  Google Scholar 

  62. Bahadure NB, Ray AK, Thethi HP (2017) Image analysis for MRI based brain tumor detection and feature extraction using biologically inspired BWT and SVM. Int J Biomed Imaging 2017:1–12. https://doi.org/10.1155/2017/9749108

    Article  Google Scholar 

  63. Chaudhary A, Bhattacharjee V (2018) An efficient method for brain tumor detection and categorization using MRI images by K-means clustering & DWT. Int J Inf Technol 12(1):141–148. https://doi.org/10.1007/s41870-018-0255-4

    Article  Google Scholar 

  64. Mallick PK, Ryu SH, Satapathy SK, Mishra S, Nguyen GN, Tiwari P (2019) Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network. IEEE Access 7:46278–46287. https://doi.org/10.1109/access.2019.2902252

    Article  Google Scholar 

  65. Kumar P, VijayKumar B (2019). Brain tumor MRI segmentation and classification using ensemble classifier. https://www.ijrte.org/wp-content/uploads/papers/v8i1s4/A10440681S419.pdf

  66. Sriramakrishnan P, Kalaiselvi T, Rajeswaran R (2019) Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine. Biocybern Biomed Eng 39(2):470–487. https://doi.org/10.1016/j.bbe.2019.02.002

    Article  Google Scholar 

  67. Marghalani BF, Arif M (2019) Automatic classification of brain tumor and Alzheimer’s disease in MRI. Procedia Comput Sci 163:78–84. https://doi.org/10.1016/j.procs.2019.12.089

    Article  Google Scholar 

  68. Deepak S, Ameer P (2019) Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med 111:103345. https://doi.org/10.1016/j.compbiomed.2019.103345

    Article  Google Scholar 

  69. Ghassemi N, Shoeibi A, Rouhani M (2020) Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomed Signal Process Control 57:101678. https://doi.org/10.1016/j.bspc.2019.101678

    Article  Google Scholar 

  70. Kurup RV, Sowmya V, Soman KP (2019) Effect of data pre-processing on brain tumor classification using capsulenet. In: ICICCT 2019—system reliability, quality control, safety, maintenance and management, pp 110–119. https://doi.org/10.1007/978-981-13-8461-5_13

  71. Eluri VR, Ramesh C, Dhipti SN, Sujatha D (2019) Analysis of MRI-based brain tumor detection using RFCM clustering and SVM classifier. In: Wang J, Reddy G, Prasad V, Reddy V (eds) Advances in intelligent systems and computing soft computing and signal processing. Springer, Singapore, pp 319–326. https://doi.org/10.1007/978-981-13-3393-4_33

    Chapter  Google Scholar 

  72. Arasi PR, Suganthi M (2019) A clinical support system for brain tumor classification using soft computing techniques. J Med Syst. https://doi.org/10.1007/s10916-019-1266-9

    Article  Google Scholar 

  73. Chandra SK, Bajpai MK (2020) Fractional mesh-free linear diffusion method for image enhancement and segmentation for automatic tumor classification. Biomed Signal Process Control 58:101841. https://doi.org/10.1016/j.bspc.2019.101841

    Article  Google Scholar 

  74. Raja PS, Rani AV (2020) Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach. Biocybern Biomed Eng 40(1):440–453. https://doi.org/10.1016/j.bbe.2020.01.006

    Article  Google Scholar 

  75. Hamid MA, Khan NA (2020) Investigation and classification of MRI brain tumors using feature extraction technique. J Med Biol Eng 40(2):307–317. https://doi.org/10.1007/s40846-020-00510-1

    Article  Google Scholar 

  76. Çinar A, Yildirim M (2020) Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Med Hypotheses 139:109684. https://doi.org/10.1016/j.mehy.2020.109684

    Article  Google Scholar 

  77. Begum SS, Lakshmi DR (2020) Combining optimal wavelet statistical texture and recurrent neural network for tumour detection and classification over MRI. Multimed Tools Appl 79(19–20):14009–14030. https://doi.org/10.1007/s11042-020-08643-w

    Article  Google Scholar 

  78. Burduk R, Trajdos P (2013) Construction of sequential classifier using confusion matrix. In: Saeed K, Chaki R, Cortesi A, Wierzchoń S (eds) Computer information systems and industrial management lecture notes in computer science. Springer, Berlin, pp 401–407. https://doi.org/10.1007/978-3-642-40925-7_37

    Chapter  Google Scholar 

  79. Rashid MHO, Mamun MA, Hossain MA, Uddin MP (2018) Brain tumor detection using anisotropic filtering, SVM classifier and morphological operation from MR images. In: International conference on computer, communication, chemical, material and electronic engineering, IC4ME2 2018, pp 3–6. https://doi.org/10.1109/IC4ME2.2018.8465613

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    Vatika Jalali & Dapinder Kaur

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Jalali, V., Kaur, D. A study of classification and feature extraction techniques for brain tumor detection. Int J Multimed Info Retr 9, 271–290 (2020). https://doi.org/10.1007/s13735-020-00199-7

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