Abstract
The detection and brain tumor (BT) segmentation and classification are mandatory steps before any radiotherapy or surgery. When performed manually, segmentation is time-consuming and exposed to human errors. Therefore, significant efforts have been made to automate the process. In this study, a proposed automatic discriminative learning-based approach for brain tumor classification and segmentation using a metaheuristic optimizer called Sparrow Search Algorithm (SpaSA). The segmentation process is performed using UNet models (i.e., U-Net, U-Net++, Attention U-Net, and V-net). Additionally, the learning and SpaSA optimization is performed using pre-trained CNN models (i.e., MobileNet, MobileNetV2, MobileNetV3Small, MobileNetV3Large, EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5,VGG16, and VGG19). To optimize the training hyperparameters, the SpaSA metaheuristic optimizer is used. The dataset is collected from 6 public sources. Two types of datasets are generated. One with 2-classes and the other with 4-classes. The best-reported scores by U-Net architecture are 99.73% accuracy, 99.93% specificity, 99.35% AUC, 99.78% IoU, and 99.80% Dice for the whole tumor region. For the 2-classes dataset, the best reported overall accuracy from the applied CNN experiments is 99.99% by the MobileNetV3 Large pre-trained model. The average accuracy is 99.92%. Similarly, For the 4-classes dataset, the best reported overall accuracy from the applied CNN experiments is 99.73% by the EfficientNetB2 pre-trained model. The average accuracy is 99.19%. The suggested approach is compared with 11 related studies.
Similar content being viewed by others
Data availability
The datasets, if existing, that are used, generated, or analyzed during the current study (A) if the datasets are owned by the authors, they are available from the corresponding author on reasonable request, (B) if the datasets are not owned by the authors, the supplementary information including the links and sizes are included in this published article.
References
Abdulazeem Y, Balaha HM, Bahgat WM, Badawy M (2021) Human action recognition based on transfer learning approach. IEEE Access
Avni U, Greenspan H, Konen E, Sharon M, Goldberger J (2010) X-ray categorization and retrieval on the organ and pathology level, using patch-based visual words. IEEE Trans Med Imaging 30(3):733–746
Baghdadi NA et al (2022) Classification of breast cancer using a manta-ray foraging optimized transfer learning framework. PeerJ Comput Sci 8:e1054
Baghdadi NA et al (2022) An optimized deep learning approach for suicide detection through Arabic tweets. PeerJ Comput Sci 8:e1070
Baghdadi NA et al (2022) An automated diagnosis and classification of covid-19 from chest ct images using a transfer learning-based convolutional neural network. Comput Biol Med 144:105383
Baghdadi NA, Malki A, Balaha HM, Badawy M, Elhosseini M (2022) A3c-tl-gto: Alzheimer automatic accurate classification using transfer learning and artificial gorilla troops optimizer. Sensors 22(11):4250
Bahgat WM, Balaha HM, AbdulAzeem Y, Badawy MM (2021) An optimized transfer learning-based approach for automatic diagnosis of covid-19 from chest x-ray images. PeerJ Comput Sci 7:e555
Balaha HM, Saafan MM (2021) Automatic exam correction framework (aecf) for the mcqs, essays, and equations matching. IEEE Access 9:32368–32389
Balaha HM, Ali HA, Badawy M (2021) Automatic recognition of handwritten Arabic characters: a comprehensive review. Neural Comput Appl 33(7):3011–3034
Balaha HM, Ali HA, Saraya M, Badawy M (2021) A new Arabic handwritten character recognition deep learning system (ahcr-dls). Neural Comput Appl 33(11):6325–6367
Balaha HM, Balaha MH, Ali HA (2021) Hybrid covid-19 segmentation and recognition framework (hmb-hcf) using deep learning and genetic algorithms. Artif Intell Med 102156
Balaha HM, El-Gendy EM, Saafan MM (2021) Covh2sd: A covid-19 detection approach based on Harris hawks optimization and stacked deep learning. Expert Syst Appl 186:115805
Balaha HM, et al (2021) Recognizing Arabic handwritten characters using deep learning and genetic algorithms. Multimed Tools Appl 1–37
Balaha HM, Saif M, Tamer A, Abdelhay EH (2022) Hybrid deep learning and genetic algorithms approach (hmb-dlgaha) for the early ultrasound diagnoses of breast cancer. Neural Comput Appl 34(11):8671–8695
Balaha HM, Shaban AO, El-Gendy EM, Saafan MM (2022) A multi-variate heart disease optimization and recognition framework. Neural Comput Appl 1–38
Balaha HM, El-Gendy EM, Saafan MM (2022) A complete framework for accurate recognition and prognosis of covid-19 patients based on deep transfer learning and feature classification approach. Artif Intell Rev 1–46
Balaha MM, et al (2022) A vision-based deep learning approach for independent-users Arabic sign language interpretation. Multimed Tools Appl, pp 1–20
Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures In: Proceedings of ICML workshop on unsupervised and transfer learning. (JMLR Workshop and Conference Proceedings), pp 37–49
Beers A, et al (2017) Sequential 3d u-nets for biologically-informed brain tumor segmentation. arXiv:1709.02967
Bernal J et al (2019) Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review. Artif Intell Med 95:64–81
Binaghi E, et al (2014) Automatic segmentation of mr brain tumor images using support vector machine in combination with graph cut. In IJCCI (NCTA), pp 152–157
Bosch A, Munoz X, Oliver A, Marti J (2006) Modeling and classifying breast tissue density in mammograms In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06), vol 2. IEEE, pp 1552–1558
Bradley AP (1997) The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn 30(7):1145–1159
Caselles V, Catté F, Coll T, Dibos F (1993) A geometric model for active contours in image processing. Numer Math 66(1):1–31
Cates JE, Lefohn AE, Whitaker RT (2004) Gist: an interactive, gpu-based level set segmentation tool for 3d medical images. Med Image Anal 8(3):217–231
Cates JE, Whitaker RT, Jones GM (2005) Case study: an evaluation of user-assisted hierarchical watershed segmentation. Med Image Anal 9(6):566–578
Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848
Cheng J et al (2015) Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE 10(10):e0140381
Cheng J et al (2016) Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PLoS ONE 11(6):e0157112
Chollet F (2017) Xception: deep learning with depthwise separable convolutions In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258
Çinar A, Yildirim M (2020) Detection of tumors on brain mri images using the hybrid convolutional neural network architecture. Med Hypoth 139:109684
Ciregan D, Meier U, Schmidhuber J (2012) Multi-column deep neural networks for image classification In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 3642–3649
Clark MC et al (1998) Automatic tumor segmentation using knowledge-based techniques. IEEE Trans Med Imag 17(2):187–201
Cogswell M, Ahmed F, Girshick R, Zitnick L, Batra D (2015) Reducing overfitting in deep networks by decorrelating representations. arXiv:1511.06068
Collins DL, Holmes CJ, Peters TM, Evans AC (1995) Automatic 3-d model-based neuroanatomical segmentation. Hum Brain Map 3(3):190–208
Courbariaux M, Hubara I, Soudry D, El-Yaniv R, Bengio Y (2016) Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv:1602.02830
Das S, Aranya ORR, Labiba NN (2019) Brain tumor classification using convolutional neural network In: 2019 1st International conference on advances in science, engineering and robotics technology (ICASERT). IEEE, pp 1–5
Dauphin YN, De Vries H, Bengio Y (2015) Equilibrated adaptive learning rates for non-convex optimization. arXiv:1502.04390
Davies E, Clarke C (2004) Early symptoms of brain tumours. J Neurol Neurosurg Psychiatry 75(8):1205–1206
DeAngelis LM (2001) Brain tumors. N Engl J Med 344(2):114–123
Deepak S, Ameer P (2019) Brain tumor classification using deep cnn features via transfer learning. Comput Biol Med 111:103345
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302
Doyle S, Vasseur F, Dojat M, Forbes F (2013) Fully automatic brain tumor segmentation from multiple mr sequences using hidden Markov fields and variational em. In: Procs. NCI-MICCAI BraTS, pp 18–22
Drevelegas A, Papanikolaou N (2011) Imaging modalities in brain tumors In: Imaging of brain tumors with histological correlations. Springer, Berlin, pp 13–33
Díaz-Pernas FJ, Martínez-Zarzuela M, Antón-Rodríguez M, González-Ortega D (2021) A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network In: Healthcare, vol 9. Multidisciplinary Digital Publishing Institute, p 153
El-Gendy EM, Saafan MM, Elksas MS, Saraya SF, Areed FF (2019) New suggested model reference adaptive controller for the divided wall distillation column. Ind Eng Chem Res 58(17):7247–7264
Fletcher-Heath LM, Hall LO, Goldgof DB, Murtagh FR (2001) Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif Intell Med 21(1–3):43–63
Foo JL (2006) A survey of user interaction and automation in medical image segmentation methods In: Tech rep ISUHCI20062, Human Computer Interaction Department, Iowa State Univ
Fu Y, Li C, Yu FR, Luan TH, Zhang Y (2021) A survey of driving safety with sensing, vehicular communications, and artificial intelligence-based collision avoidance. IEEE Trans Intell Transport Syst
Fukushima K, Miyake S (1982) Neocognitron: a self-organizing neural network model for a mechanism of visual pattern recognition In: Competition and cooperation in neural nets. Springer, New York, pp 267–285
Geremia E et al (2011) Spatial decision forests for ms lesion segmentation in multi-channel magnetic resonance images. NeuroImage 57(2):378–390
Gerig G, Jomier M, Chakos M (2001) Valmet: A new validation tool for assessing and improving 3d object segmentation In: International conference on medical image computing and computer-assisted intervention. Springer, New York, pp 516–523
Gibbs P, Buckley DL, Blackband SJ, Horsman A (1996) Tumour volume determination from mr images by morphological segmentation. Phys Med Biol 41(11):2437
Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks In: Proceedings of the fourteenth international conference on artificial intelligence and statistics. JMLR workshop and conference proceedings, pp 315–323
Goetz M, et al (2014) Extremely randomized trees based brain tumor segmentation. In: Proceeding of BRATS challenge-MICCAI, pp 006–011
Gordillo N, Montseny E, Sobrevilla P (2013) State of the art survey on mri brain tumor segmentation. Magnet Reson Imag 31(8):1426–1438
Hamada A Br35h: brain tumor detection. https://www.kaggle.com/ahmedhamada0/brain-tumor-detection. Accessed 01 Sept 2021
Hamamci A, Kucuk N, Karaman K, Engin K, Unal G (2011) Tumor-cut: segmentation of brain tumors on contrast enhanced mr images for radiosurgery applications. IEEE Trans Med Imag 31(3):790–804
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology 143(1):29–36
Havaei M et al (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Hinton GE (2009) Deep belief networks. Scholarpedia 4(5):5947
Hou L, et al (2016) Patch-based convolutional neural network for whole slide tissue image classification In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2424–2433
Howard AG, et al (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861
Hu J, Mou L, Schmitt A, Zhu XX (2017) Fusionet: a two-stream convolutional neural network for urban scene classification using polar and hyperspectral data In: 2017 Joint urban remote sensing event (JURSE). IEEE, pp 1–4
Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708
Hubel DH, Wiesel TN (1968) Receptive fields and functional architecture of monkey striate cortex. J Physiol 195(1):215–243
Jaccard P (1912) The distribution of the flora in the alpine zone. 1. New Phytol 11(2):37–50
JEAN_DJHONSON Brain mri images for brain tumor detection. https://www.kaggle.com/jjprotube/brain-mri-images-for-brain-tumor-detection. Accessed 01 Sept 2021
Jiang J et al (2013) 3d brain tumor segmentation in multimodal mr images based on learning population-and patient-specific feature sets. Comput Med Imag Graph 37(7–8):512–521
John P et al (2012) Brain tumor classification using wavelet and texture based neural network. Int J Sci Eng Res 3(10):1–7
Kaus MR et al (2001) Automated segmentation of mr images of brain tumors. Radiology 218(2):586–591
Khawaldeh S, Pervaiz U, Rafiq A, Alkhawaldeh RS (2018) Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks. Appl Sci 8(1):27
Khotanlou H, Colliot O, Atif J, Bloch I (2009) 3d brain tumor segmentation in mri using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets Syst 160(10):1457–1473
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980
Kleesiek J, et al (2014) Ilastik for multi-modal brain tumor segmentation. In: Proceedings MICCAI BraTS (brain tumor segmentation challenge) pp 12–17
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105
Kwon D, Shinohara RT, Akbari H, Davatzikos C (2014) Combining generative models for multifocal glioma segmentation and registration In: International conference on medical image computing and computer-assisted intervention. Springer, pp 763–770
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. nature 521(7553):436–444
Lefohn AE, Cates JE, Whitaker RT (2003) Interactive, gpu-based level sets for 3d segmentation In: International conference on medical image computing and computer-assisted intervention. Springer, New York, pp 564–572
Letteboer MM et al (2004) Segmentation of tumors in magnetic resonance brain images using an interactive multiscale watershed algorithm1. Acad Radiol 11(10):1125–1138
Lin M, Chen Q, Yan S (2013) Network in network. arXiv:1312.4400
Liu B et al (2017) Supervised deep feature extraction for hyperspectral image classification. IEEE Trans Geosci Remote Sens 56(4):1909–1921
Liu T, Yuan Z, Wu L, Badami B (2021) An optimal brain tumor detection by convolutional neural network and enhanced sparrow search algorithm. Proc Inst Mech Eng Part H 235(4):459–469
Liu T, Yuan Z, Wu L, Badami B (2021) Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm. Int J Imag Syst Technol 31(4):1921–1935
Lu J et al (2015) Transfer learning using computational intelligence: a survey. Knowled Based Syst 80:14–23
Ma L, Cheng S, Shi Y (2020) Enhancing learning efficiency of brain storm optimization via orthogonal learning design. IEEE Trans Syst Man Cybernet Syst 51(11):6723–6742
Ma L, Huang M, Yang S, Wang R, Wang X (2021) An adaptive localized decision variable analysis approach to large-scale multiobjective and many-objective optimization. IEEE Trans Cybernet
McMahan HB, et al (2013) Ad click prediction: a view from the trenches In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1222–1230
Menze BH, et al. (2010) A generative model for brain tumor segmentation in multi-modal images In: International conference on medical image computing and computer-assisted intervention. Springer, New York, pp 151–159
Menze BH et al (2014) The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans Med Imaging 34(10):1993–2024
Miikkulainen R, et al (2019) Evolving deep neural networks In: Artificial intelligence in the age of neural networks and brain computing. Elsevier, Amsterdam, pp 293–312
Milletari F, Navab N, Ahmadi SA (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation In: 2016 fourth international conference on 3D vision (3DV). IEEE, pp 565–571
Moon N, Bullitt E, Van Leemput K, Gerig G (2002) Model-based brain and tumor segmentation In: Object recognition supported by user interaction for service robots, vol 1. IEEE, pp 528–531
Mukkamala MC, Hein M (2017) Variants of rmsprop and adagrad with logarithmic regret bounds In: International conference on machine learning. PMLR, pp 2545–2553
Nair V, Hinton GE (2010) Rectified linear units improve restricted Boltzmann machines. In: Icml
Nascimento AM et al (2019) A systematic literature review about the impact of artificial intelligence on autonomous vehicle safety. IEEE Trans Intell Transp Syst 21(12):4928–4946
Oktay O, et al (2018) Attention u-net: Learning where to look for the pancreas. arXiv:1804.03999
Ostrom QT et al (2020) Cbtrus statistical report: primary brain and other central nervous system tumors diagnosed in the united states in 2013–2017. Neuro-oncology 22(Supplement–1):iv1–iv96
Ozkan M, Dawant BM, Maciunas RJ (1993) Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study. IEEE Trans Med Imag 12(3):534–544
Panigrahi A, Brain tumor detection mri. https://www.kaggle.com/abhranta/brain-tumor-detection-mri. Accessed 01 Sept 2021
Papageorgiou E et al (2008) Brain tumor characterization using the soft computing technique of fuzzy cognitive maps. Appl Soft Comput 8(1):820–828
Pereira S, Pinto A, Alves V, Silva CA (2015) Deep convolutional neural networks for the segmentation of gliomas in multi-sequence mri In: BrainLes 2015. Springer, New York, pp 131–143
Popuri K, Cobzas D, Murtha A, Jägersand M (2012) 3d variational brain tumor segmentation using Dirichlet priors on a clustered feature set. Int J Comput Assist Radiol Surg 7(4):493–506
Powers DM (2020) Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. arXiv:2010.16061
Prastawa M, Bullitt E, Ho S, Gerig G (2004) A brain tumor segmentation framework based on outlier detection. Med Image Anal 8(3):275–283
Prastawa M, Bullitt E, Moon N, Van Leemput K, Gerig G (2003) Automatic brain tumor segmentation by subject specific modification of atlas priors1. Acad Radiol 10(12):1341–1348
Prastawa M, Bullitt E, Ho S, Gerig G (2003) Robust estimation for brain tumor segmentation In: International conference on medical image computing and computer-assisted intervention. Springer, New York, pp 530–537
Rajan P, Sundar C (2019) Brain tumor detection and segmentation by intensity adjustment. J Med Syst 43(8):1–13
Ramachandran P, Zoph B, Le QV (2017) Searching for activation functions. arXiv:1710.05941
Rao V, Sarabi MS, Jaiswal A (2015) Brain tumor segmentation with deep learning. In: MICCAI multimodal brain tumor segmentation challenge (BraTS) 59
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation In: International conference on medical image computing and computer-assisted intervention. Springer, New York, pp 234–241
Ruder S (2016) An overview of gradient descent optimization algorithms. arXiv:1609.04747
Saafan MM, El-Gendy EM (2021) Iwossa: an improved whale optimization salp swarm algorithm for solving optimization problems. Expert Syst Appl 176:114901
Saouli R, Akil M, Kachouri R et al (2018) Fully automatic brain tumor segmentation using end-to-end incremental deep neural networks in mri images. Comput Methods Programs Biomed 166:39–49
Sartaj Brain tumor classification (mri). https://www.kaggle.com/sartajbhuvaji/brain-tumor-classification-mri. Accessed 01 Sept 2021
Saxena P, Maheshwari A, Maheshwari S (2021) Predictive modeling of brain tumor: a deep learning approach. In: Innovations in computational intelligence and computer vision. Springer, New York, pp 275–285
Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673–2681
Selvaraj H, Selvi ST, Selvathi D, Gewali L (2007) Brain mri slices classification using least squares support vector machine. Int J Intell Comput Med Sci Image Process 1(1):21–33
Sikka K, Sinha N, Singh PK, Mishra AK (2009) A fully automated algorithm under modified fcm framework for improved brain mr image segmentation. Magnet Reson Imaging 27(7):994–1004
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
Smith SM et al (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31(4):1487–1505
Stadlbauer A et al (2004) Improved delineation of brain tumors: an automated method for segmentation based on pathologic changes of 1h-mrsi metabolites in gliomas. Neuroimage 23(2):454–461
Stupp R, Tonn JC, Brada M, Pentheroudakis G (2010) High-grade malignant glioma: Esmo clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 21:v190–v193
Subbanna N, Precup D, Arbel T (2014) Iterative multilevel mrf leveraging context and voxel information for brain tumour segmentation in mri In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 400–405
Summers D (2003) Harvard whole brain atlas: www.med.harvard.edu/aanlib/home.html. J Neurol Neurosurg Psychiatry 74(3):288
Tato A, Nkambou R (2018) Improving Adam optimizer
Thombre S, et al. (2020) Sensors and ai techniques for situational awareness in autonomous ships: a review. IEEE Trans Intell Transport Syst
Ullah Z, Farooq MU, Lee SH, An D (2020) A hybrid image enhancement based brain mri images classification technique. Med Hypoth 143:109922
Urban G, Bendszus M, Hamprecht F, Kleesiek J (2014) Multi-modal brain tumor segmentation using deep convolutional neural networks. In: MICCAI BraTS (brain tumor segmentation) challenge. Proceedings, winning contribution, pp 31–35
Viradiya P, Brian tumor dataset. https://www.kaggle.com/preetviradiya/brian-tumor-dataset. Accessed 01 Sept 2021
von Deimling A (2009) Gliomas, vol 171. Springer, Berlin
Ward R, Wu X, Bottou L (2019) Adagrad stepsizes: sharp convergence over nonconvex landscapes In: International conference on machine learning. PMLR, pp 6677–6686
Webb J et al (1999) Automatic detection of hippocampal atrophy on magnetic resonance images. Magnet Reson Imag 17(8):1149–1161
White DR, Houston AS, Sampson WF, Wilkins GP (1999) Intra-and interoperator variations in region-of-interest drawing and their effect on the measurement of glomerular filtration rates. Clin Nucl Med 24(3):177–181
Who health organization (2021) cancer today. https://gco.iarc.fr/today/online-analysis-map. Accessed 01 Sept 2021
Xiang T, Wang J, Liao X (2007) An improved particle swarm optimizer with momentum In: 2007 IEEE congress on evolutionary computation. (IEEE), pp 3341–3345
Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control Eng 8(1):22–34
Yang W et al (2012) Content-based retrieval of focal liver lesions using bag-of-visual-words representations of single-and multiphase contrast-enhanced ct images. J Digital Imag 25(6):708–719
Yousif NR, Balaha HM, Haikal AY, El-Gendy EM (2022) A generic optimization and learning framework for parkinson disease via speech and handwritten records. J Ambient Intell Hum Comput 1–21
Zeiler MD (2012) Adadelta: an adaptive learning rate method. arXiv:1212.5701
Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) Unet++: a nested u-net architecture for medical image segmentation In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, New York, pp 3–11
Zikic D, Ioannou Y, Brown M, Criminisi A (2014) Segmentation of brain tumor tissues with convolutional neural networks. Proc MICCAI-BRATS 36:36–39
Zikic D, et al (2012) Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel mr In: International conference on medical image computing and computer-assisted intervention. Springer, New York, pp 369–376
Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8697–8710
Funding
No funding was received for this work (i.e., study).
Author information
Authors and Affiliations
Contributions
All the authors have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Conflict of Interest
No conflict of interest exists. We wish to confirm that, there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.
Ethical approval
We further confirm, if existing, that any aspect of the work covered in this manuscript that has involved human patients has been conducted with the ethical approval of all relevant bodies and that such approvals are acknowledged within the manuscript. Written consent to publish potentially identifying information, such as details of the case and photographs, was obtained from the patient(s) or their legal guardian(s).
Informed Consent
There is no informed consent for the current study.
Consent for Publication
Not Applicable.
Research involving human and animal rights
The current study does not contain any studies with human participants and/or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Balaha, H.M., Hassan, A.ES. A variate brain tumor segmentation, optimization, and recognition framework. Artif Intell Rev 56, 7403–7456 (2023). https://doi.org/10.1007/s10462-022-10337-8
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-022-10337-8