Brain cancer is a disease caused by the growth of abnormal aggressive cells in the brain outside of normal cells. Symptoms and diagnosis of brain cancer cases are producing more accurate results day by day in parallel with the development of technological opportunities. In this study, a deep learning model called BrainMRNet which is developed for mass detection in open-source brain magnetic resonance images was used. The BrainMRNet model includes three processing steps: attention modules, the hypercolumn technique, and residual blocks. To demonstrate the accuracy of the proposed model, three types of tumor data leading to brain cancer were examined in this study: glioma, meningioma, and pituitary. In addition, a segmentation method was proposed, which additionally determines in which lobe area of the brain the two classes of tumors that cause brain cancer are more concentrated. The classification accuracy rates were performed in the study; it was 98.18% in glioma tumor, 96.73% in meningioma tumor, and 98.18% in pituitary tumor. At the end of the experiment, using the subset of glioma and meningioma tumor images, it was determined which at brain lobe the tumor region was seen, and 100% success was achieved in the analysis of this determination. In this study, a hybrid deep learning model is presented to determine the detection of the brain tumor. In addition, open-source software was proposed, which statistically found in which lobe region of the human brain the brain tumor occurred. The methods applied and tested in the experiments have shown promising results with a high level of accuracy, precision, and specificity. These results demonstrate the availability of the proposed approach in clinical settings to support the medical decision regarding brain tumor detection.
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Bisdas S, D’Arco F (2019) In: Barkhof F, Jager R, Thurnher M, Rovira Cañellas A (eds) Pediatric tumor neuroradiology BT - clinical neuroradiology: the ESNR textbook. Springer International Publishing, Cham, pp 1–80
Marszałek A, Szylberg Ł, Wiśniewski S (2016) Pathologic aspects of skull base tumors. Reports Pract Oncol Radiother J Gt Cancer Cent Pozn Polish Soc Radiat Oncol 21:288–303. https://doi.org/10.1016/j.rpor.2016.02.006
Lah TT, Novak M, Breznik B (2020) Brain malignancies: glioblastoma and brain metastases. Semin Cancer Biol 60:262–273. https://doi.org/10.1016/j.semcancer.2019.10.010
Wang J, Pulido JS, O’Neill BP, Johnston PB (2015) Second malignancies in patients with primary central nervous system lymphoma. Neuro-Oncology 17:129–135. https://doi.org/10.1093/neuonc/nou105
(2019) Brain and other nervous system cancer — cancer stat facts. In: Natl. Cancer Instıtute https://seer.cancer.gov/statfacts/html/brain.html.
(2019) Brain tumor: statistics | Cancer.Net. In: Am. Cancer Soc. https://www.cancer.net/cancer-types/brain-tumor/statistics. Accessed 4 Feb 2020
Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7–34. https://doi.org/10.3322/caac.21551
Minniti G, Filippi AR, Osti MF, Ricardi U (2017) Radiation therapy for older patients with brain tumors. Radiat Oncol 12:101. https://doi.org/10.1186/s13014-017-0841-9
Hanif F, Muzaffar K, Perveen K et al (2017) Glioblastoma multiforme: a review of its epidemiology and pathogenesis through clinical presentation and treatment. Asian Pac J Cancer Prev 18:3–9. https://doi.org/10.22034/APJCP.2017.18.1.3
Sultan HH, Salem NM, Al-Atabany W (2019) Multi-classification of brain tumor images using deep neural network. IEEE Access 7:69215–69225. https://doi.org/10.1109/ACCESS.2019.2919122
Taylor OG, Brzozowski JS, Skelding KA (2019) Glioblastoma multiforme: an overview of emerging therapeutic targets. Front Oncol 9:963. https://doi.org/10.3389/fonc.2019.00963
Thon N, Tonn J-C, Kreth F-W (2019) The surgical perspective in precision treatment of diffuse gliomas. Onco Targets Ther 12:1497–1508. https://doi.org/10.2147/OTT.S174316
Lemée J-M, Corniola MV, Da Broi M et al (2019) Extent of resection in meningioma: predictive factors and clinical implications. Sci Rep 9:5944. https://doi.org/10.1038/s41598-019-42451-z
Ferluga S, Baiz D, Hilton DA, Adams CL, Ercolano E, Dunn J, Bassiri K, Kurian KM, Hanemann CO (2020) Constitutive activation of the EGFR–STAT1 axis increases proliferation of meningioma tumor cells. Neuro-Oncology Adv 2. https://doi.org/10.1093/noajnl/vdaa008
Chatzellis E, Alexandraki KI, Androulakis II, Kaltsas G (2015) Aggressive pituitary tumors. Neuroendocrinology 101:87–104. https://doi.org/10.1159/000371806
Vala H (2013) In: Mesquita JR (ed) The endocrine glands in the dog: from the cell to hormone. IntechOpen, Rijeka, p Ch. 6
de Zakir JC, Casulari LA, Rosa JWC et al (2016) Prognostic value of invasion, markers of proliferation, and classification of giant pituitary tumors, in a georeferred cohort in Brazil of 50 patients, with a long-term postoperative follow-up. Int J Endocrinol 2016:7964523–7964514. https://doi.org/10.1155/2016/7964523
Trouillas J, Jaffrain-Rea ML, Vasiljevic A, Raverot G, Roncaroli F, Villa C (2020) How to classify the pituitary neuroendocrine tumors (PitNET)s in 2020. Cancers (Basel) 12:1–17. https://doi.org/10.3390/cancers12020514
(2019) Pituitary tumor: MedlinePlus Medical Encyclopedia. In: U.S. Natl. Libr. Med. https://medlineplus.gov/ency/article/000704.htm. Accessed 4 Feb 2020
Iqbal S, Khan MUG, Saba T, Rehman A (2017) Computer-assisted brain tumor type discrimination using magnetic resonance imaging features. Biomed Eng Lett 8:5–28. https://doi.org/10.1007/s13534-017-0050-3
Gupta N, Bhatele P, Khanna P (2019) Glioma detection on brain MRIs using texture and morphological features with ensemble learning. Biomed Signal Proc Cont 47:115–125. https://doi.org/10.1016/j.bspc.2018.06.003
Tamrazi B, Shiroishi MS, Liu C-SJ (2016) Advanced imaging of intracranial meningiomas. Neurosurg Clin N Am 27:137–143. https://doi.org/10.1016/j.nec.2015.11.004
Nour M, Cömert Z, Polat K (2020) A novel medical diagnosis model for COVID-19 infection detection based on deep features and Bayesian optimization. Appl soft Comput 106580. https://doi.org/10.1016/j.asoc.2020.106580
Ahuja AS (2019) The impact of artificial intelligence in medicine on the future role of the physician. PeerJ 7:e7702–e7702. https://doi.org/10.7717/peerj.7702
Toğaçar M, Özkurt KB, Ergen B, Cömert Z (2020) BreastNet: a novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Phys A Stat Mech its Appl 545:123592. https://doi.org/10.1016/j.physa.2019.123592
Toğaçar M, Ergen B, Cömert Z (2020) BrainMRNet: brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Med Hypotheses 134:109531. https://doi.org/10.1016/j.mehy.2019.109531
Tandel GS, Balestrieri A, Jujaray T, Khanna NN, Saba L, Suri JS (2020) Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm. Comput Biol Med 122:103804. https://doi.org/10.1016/j.compbiomed.2020.103804
Meola A, Rao J, Chaudhary N, Sharma M, Chang SD (2018) Gold nanoparticles for brain tumor imaging: a systematic review. Front Neurol 9:328. https://doi.org/10.3389/fneur.2018.00328
Mohsen H, El-Dahshan E-SA, El-Horbaty E-SM, Salem A-BM (2018) Classification using deep learning neural networks for brain tumors. Futur Comput Informatics J 3:68–71. https://doi.org/10.1016/j.fcij.2017.12.001
Pushpa BR, Louies F (2019) Detection and classification of brain tumor using machine learning approaches. Int J Res Pharm Sci 10:2153–2162. https://doi.org/10.26452/ijrps.v10i3.1442
Byale H, Lingaraju GM, Sivasubramanian S (2018) Automatic segmentation and classification of brain tumor using machine learning techniques. Int J Appl Eng Res 13:11686–11692
Sawant A, Bhandari M, Yadav R et al (2018) Brain cancer detection from MRI: a machine learning approach (tensorflow). Int Res J Eng Technol 05:2089
Ahammed Muneer KV, Paul Joseph K (2019) Automation of MR brain image classification for malignancy detection. J Mech Med Biol 19:1–18. https://doi.org/10.1142/S0219519419400025
Kohl SAA, Romera-Paredes B, Meyer C, et al (2018) A probabilistic U-net for segmentation of ambiguous images. Adv Neural Inf Process Syst 2018-Decem:6965–6975
Çiçek Ö, Abdulkadir A, Lienkamp SS, et al (2016) 3D U-net: learning dense volumetric segmentation from sparse annotation. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 9901 lncs:424–432. https://doi.org/10.1007/978-3-319-46723-8_49
Ding Y, Acosta R, Enguix V, Suffren S, Ortmann J, Luck D, Dolz J, Lodygensky GA (2020) Using deep convolutional neural networks for neonatal brain image segmentation. Front Neurosci 14:207. https://doi.org/10.3389/fnins.2020.00207
Mahbod A, Wang C, Chowdhury M, Smedby Ö (2016) Brain segmentation using artificial neural networks with shape context. Under Prep:1–29
Schlemper J, Oktay O, Schaap M, Heinrich M, Kainz B, Glocker B, Rueckert D (2019) Attention gated networks: learning to leverage salient regions in medical images. Med Image Anal 53:197–207. https://doi.org/10.1016/j.media.2019.01.012
Germain H, Bourmaud G, Lepetit V (2019) Sparse-to-dense hypercolumn matching for long-term visual localization. Proc - 2019 Int Conf 3D Vision, 3DV 2019 513–523. https://doi.org/10.1109/3DV.2019.00063
Cheng J (2017) Brain tumor dataset. https://doi.org/10.6084/m9.figshare.1512427.v5
Cheng J, Yang W, Huang M, Huang W, Jiang J, Zhou Y, Yang R, Zhao J, Feng Y, Feng Q, Chen W (2016) Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PLoS One 11:e0157112
(2019) Converting image. In: GitHub web. https://github.com/shimat/opencvsharp/wiki/Converting-Image. Accessed 5 Feb 2020
Solda F, Fersht N (2017) Radiotherapy for pituitary tumours. NCBI
Toğaçar M (2020) The BrainMRNet model. GitHub, San Francisco https://github.com/happytgcr/brainMRNet.
Aguiar JA, Gong ML, Unocic RR et al (2019) Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning. Sci Adv 5:eaaw1949. https://doi.org/10.1126/sciadv.aaw1949
Meng Z, Li L, Jiao L, Feng Z, Tang X, Liang M (2019) Fully dense multiscale fusion network for hyperspectral image classification. Remote Sens 11:1–20. https://doi.org/10.3390/rs11222718
Chao H, Fenhua W, Ran Z (2019) Sign language recognition based on CBAM-ResNet. In: Proceedings of the 2019 International Conference on Artificial Intelligence and Advanced Manufacturing. ACM, New York, NY, USA, pp 48:1--48:6
Woo S, Park J, Lee J-Y, Kweon IS CBAM: convolutional block attention module. Eccv2018
Xie S, Girshick R, Dollár P et al (2016) Aggregated residual transformations for deep neural networks. https://doi.org/10.1109/cvpr.2017.634
Yue B, Fu J, Liang J (2018) Residual recurrent neural networks for learning sequential representations. Inf 9. https://doi.org/10.3390/info9030056
Chen G, Chen P, Shi Y, et al (2019) Rethinking the usage of batch normalization and dropout in the training of deep neural networks
Ramachandram DW Taylor G (2018) Skin lesion segmentation using deep hypercolumn descriptors
Hariharan B, Arbeláez P, Girshick R, Malik J (2017) Object instance segmentation and fine-grained localization using hypercolumns. IEEE Trans Pattern Anal Mach Intell 39:627–639. https://doi.org/10.1109/TPAMI.2016.2578328
Min J, Lee J, Ponce J, Cho M (2020) Learning to compose hypercolumns for visual correspondence
Sengur A, Budak U, Akbulut Y, et al (2019) 7 - a survey on neutrosophic medical image segmentation. In: Guo Y, Ashour ASBT-NS in MIA (eds). Academic press, pp 145–165
(2019) Image thresholding. In: OpenCV. https://docs.opencv.org/master/d7/d4d/tutorial_py_thresholding.html. Accessed 16 Feb 2020
(2019) Changing color spaces documentation. In: OpenCV. https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_colorspaces/py_colorspaces.html. Accessed 16 Feb 2020
(2019) Operations on arrays documentation. In: OpenCV. https://docs.opencv.org/2.4/modules/core/doc/operations_on_arrays.html. Accessed 16 Feb 2020
Mabrouk MS, Afify HM, Marzouk SY (2019) Fully automated computer-aided diagnosis system for micro calcifications cancer based on improved mammographic image techniques. Ain Shams Eng J 10:517–527. https://doi.org/10.1016/j.asej.2019.01.009
Zhan Y, Zhang G (2019) An improved OTSU algorithm using histogram accumulation moment for ore segmentation. Symmetry (Basel) 11:11. https://doi.org/10.3390/sym11030431
Engin N, Erman Z (2016) Düzce Üniversitesi Bilim ve Teknoloji Dergisi. Düzce Üniversitesi Bilim ve Teknol Derg 4:293–304
Tumen V, Yildirim O, Ergen B (2018) Recognition of road type and quality for advanced driver assistance systems with deep learning. Elektron ir Elektrotechnika 24:67–74. https://doi.org/10.5755/j01.eie.24.6.22293
Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21:1–13. https://doi.org/10.1186/s12864-019-6413-7
Cömert Z (2020) Fusing fine-tuned deep features for recognizing different tympanic membranes. Biocybern Biomed Eng 40:40–51. https://doi.org/10.1016/j.bbe.2019.11.001
Gumaei A, Hassan MM, Hassan MR, Alelaiwi A, Fortino G (2019) A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access 7:36266–36273. https://doi.org/10.1109/access.2019.2904145
Deepak S, Ameer PM (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
This work was supported by the Scientific Research Projects Coordination Unit of Firat University. Project number MF.20.11.
Conflict of interest
The authors declare that they have no conflict of interest.
The weblink to download the source codes of the BrainMRNet model written in the Python, the analysis results, and the source codes analyzing which lobe region of the tumor region is located and the relevant sub-dataset; https://github.com/happytgcr/brainMRNet.
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Toğaçar, M., Ergen, B. & Cömert, Z. Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks. Med Biol Eng Comput 59, 57–70 (2021). https://doi.org/10.1007/s11517-020-02290-x
- Brain tumor
- Attention module
- Magnetic resonance image
- Hypercolumn technique
- Image processing
- Medical segmentation