Conditional random field-recurrent neural network segmentation with optimized deep learning for brain tumour classification using magnetic resonance imaging

References

• Chen Y, Schönlieb CB, Liò P, et al. AI-based reconstruction for fast MRI – a systematic review and meta-analysis. Proc IEEE. 2022;110(2):224–245.
   Web of Science ®Google Scholar
• Hollingsworth KG. Reducing acquisition time in clinical MRI by data undersampling and compressed sensing reconstruction. Phys Med Biol. 2015;60(21):R297.
   PubMed Web of Science ®Google Scholar
• Roy S, Bhattacharyya D, Bandyopadhyay SK, et al. An effective method for computerized prediction and segmentation of multiple sclerosis lesions in brain MRI. Comput Methods Programs Biomed. 2017;140:307–320.
   PubMed Web of Science ®Google Scholar
• Roy S, Bandyopadhyay SK. Abnormal regions detection and quantification with accuracy estimation from MRI of brain. In the Proceeding of 2nd International Symposium on Instrumentation and Measurement, Sensor Network and Automation (IMSNA). IEEE, Toronto, ON, Canada, 2013.
   Google Scholar
• Gangopadhyay T, Halder S, Dasgupta P, et al. MTSE U-Net: an architecture for segmentation, and prediction of fetal brain and gestational age from MRI of brain. Netw Model Anal Health Inform Bioinform. 2022;11.
   Web of Science ®Google Scholar
• Roy S, Bhattacharyya D, Bandyopadhyay SK, et al. An iterative implementation of level set for precise segmentation of brain tissues and abnormality detection from MR images. IETE J Res. 2017;63(6):769–783.
   Web of Science ®Google Scholar
• Roy S, KumarBandyopadhyay S. A new method of brain tissues segmentation from MRI with accuracy estimation. Proc Comput Sci. 2016;85:362–369.
   Google Scholar
• Rammurthy D, Mahesh PK. Whale Harris hawks optimization based deep learning classifier for brain tumor detection using MRI images. J King Saud Univ Comp Inform Sci. 2020;34:3259–3272.
   Web of Science ®Google Scholar
• Chaudhary A, Bhattacharjee V. An efficient method for brain tumor detection and categorization using MRI images by K-means clustering & DWT. Int J Inf Technol . 2020;12(1):141–148.
   Google Scholar
• Amin J, Sharif M, Yasmin M, et al. A distinctive approach in brain tumor detection and classification using MRI. Pattern Recognit Lett. 2020;139:118–127.
   Web of Science ®Google Scholar
• Harish P, Baskar S. MRI based detection and classification of brain tumor using enhanced faster R-CNN and Alex Net model. Mater Today Proc. 2020.
   Google Scholar
• Hashemzehi R, Mahdavi SJS, Kheirabadi M, et al. Detection of brain tumors from MRI images base on deep learning using hybrid model CNN and NADE. Biocybern Biomed Eng. 2020;40(3):1225–1232.
   Web of Science ®Google Scholar
• Saba T, Mohamed AS, El-Affendi M, et al. Brain tumor detection using fusion of hand crafted and deep learning features. Cogn Syst Res. 2020;59:221–230.
   Web of Science ®Google Scholar
• Gali V. A novel robust controller of drug dosage for cancer chemotherapy. J Comput Mech Power Syst Contr. 2021;4:2.
   Google Scholar
• Ganeshan R. Skin cancer detection with optimized neural network via hybrid algorithm. Multimedia Res. 2020;3:2.
   Google Scholar
• Khan R. Artificial bee colony-based general adversarial network for liver cancer detection using CT images. Multimedia Res. 2020;3:4.
   Google Scholar
• Khan MA, Lali IU, Rehman A, et al. Brain tumor detection and classification: A Framework of marker–based watershed algorithm and multilevel priority features selection. Microsc Res Techn. 2019;82(6):909–922.
   PubMed Web of Science ®Google Scholar
• Khushi M, Shaukat K, Alam TM, et al. A comparative performance analysis of data resampling methods on imbalance medical data. IEEE Access. 2021;9:109960–109975.
   Web of Science ®Google Scholar
• Naseem U, Khushi M, Khan SK, et al. A comparative analysis of active learning for biomedical text mining. Appl Syst Innov. 2021;4:1.
   Google Scholar
• Li M, Kuang L, Xu S, et al. Brain tumor detection based on multimodal information fusion and convolutional neural network. IEEE Access. 2019;7:180134–180146.
   Web of Science ®Google Scholar
• Alam TM, Shaukat K, Khan WA, et al. An efficient deep learning-based skin cancer classifier for an imbalanced dataset. Diagnostics. 2022;12:9.
   Web of Science ®Google Scholar
• Alam TM, Shaukat K, Khelifi A, et al. A fuzzy inference-based decision support system for disease diagnosis. Comput J. 2022.
   Google Scholar
• Yang X, Khushi M, Shaukat K. Biomarker CA125 feature engineering and class imbalance learning improves ovarian cancer prediction. In: The Proceeding of IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), IEEE, Gold Coast, Australia, 2020.
   Google Scholar
• Bilal A, Sun G, Mazhar S, et al. Improved grey wolf optimization-based feature selection and classification using CNN for diabetic retinopathy detection. Evol Comp Mobile Sustain Netw. 2022;116:1–14.
   Google Scholar
• Bilal A, Sun G, Mazhar S. Finger-vein recognition using a novel enhancement method with convolutional neural network. J Chin Inst Eng . 2021;44(5):407–417.
   Web of Science ®Google Scholar
• Bilal A, Sun G, Mazhar S. Diabetic retinopathy detection using weighted filters and classification using CNN. In: The proceeding of International Conference on Intelligent Technologies (CONIT), IEEE, Hubli, India, 2021.
   Google Scholar
• Bilal A, Zhu L, Deng A, et al. AI-based automatic detection and classification of diabetic retinopathy using U-Net and deep learning. Symmetry. 2022;14(7):1–19.
   Google Scholar
• Bilal A, Sun G, Li Y, et al. Lung nodules detection using weighted filters and classification using CNN. J Chin Inst Eng . 2022;45(2):175–186.
   Web of Science ®Google Scholar
• Bilal A, Sun G, Mazhara S, et al. A transfer learning and U-Net-based automatic detection of diabetic retinopathy from fundus images. Comp Meth Biomech Biomed Eng Imag Visual. 2022;10(6):663–674.
   Web of Science ®Google Scholar
• Garg G, Garg R. Brain tumor detection and classification based on hybrid ensemble classifier. arXiv preprint arXiv:2101.00216, 2021.
   Google Scholar
• Bilal A, Sun G, Mazhar S. Survey on recent developments in automatic detection of diabetic retinopathy. J Franç Ophtal. 2021;44(3):420–440.
   PubMed Web of Science ®Google Scholar
• Bilal A, Sun G, Li Y, et al. Diabetic retinopathy detection and classification using mixed models for a disease grading database. IEEE Access. 2021;9:23544–23553.
   Web of Science ®Google Scholar
• Bonte S, Goethals I, Van RH. Machine learning based brain tumour segmentation on limited data using local texture and abnormality. Comput Biol Med. 2018;98:39–47.
   PubMed Web of Science ®Google Scholar
• Shaukat K, Luo S, Chen S, et al. Cyber threat detection using machine learning techniques: a performance evaluation perspective. In: The proceeding of International Conference on Cyber Warfare and Security (ICCWS), IEEE, Islamabad, Pakistan, 2020.
   Google Scholar
• Elaff I. Comparative study between spatio-temporal models for brain tumor growth. Biochem Biophys Res Commun 2018;496(4):1263–1268.
   PubMed Web of Science ®Google Scholar
• Ananthi VP, Balasubramaniam P, Kalaiselvi T. A new fuzzy clustering algorithm for the segmentation of brain tumor. Soft Comput. 2016;20(12):4859–4879.
   Web of Science ®Google Scholar
• Wu W, Chen AYC, Zhao L. Brain tumor detection and segmentation in a CRF framework with pixel-pairwise affinity and super pixel-level features. Int J Comput Assist Radiol Surg. 2014;9(2):241–253.
   PubMed Web of Science ®Google Scholar
• Bilal A, Sun G. Neuro-optimized numerical solution of non-linear problem based on Flierl–Petviashivili equation. SN Appl Sci. 2020;2:7.
   Web of Science ®Google Scholar
• Bilal A, Sun G, Mazhar S, et al. Neuro-optimized numerical treatment of HIV infection model. Int J Biomath. 2021;14:5.
   Web of Science ®Google Scholar
• Roy S, Meena T, Lim S-J. Demystifying supervised learning in healthcare 4.0: a new reality of transforming diagnostic medicine. Diagnostics. 2022;12:10.
   Web of Science ®Google Scholar
• Devnath L, Luo S, Summons P, et al. Deep ensemble learning for the automatic detection of pneumoconiosis in coal worker’s chest X-ray radiography. J Clin Med. 2022;11:18.
   Web of Science ®Google Scholar
• Shaukat K, Luo S, Varadharajan V. A novel method for improving the robustness of deep learning-based malware detectors against adversarial attacks. Eng Appl Artif Intell. 2022;116.
   Web of Science ®Google Scholar
• Dar KS, Luo S, Varadharajan V, et al. Performance comparison and current challenges of using machine learning techniques in cybersecurity. Energies. 2020;13:10.
   Web of Science ®Google Scholar
• Shaukat K, Luo S, Varadharajan V, et al. A survey on machine learning techniques for cyber security in the last decade. IEEE Access. 2020;8:222310–222354.
   Google Scholar
• Luo S, Shaukat K. Computational methods for medical and cyber security. In: Book: Applied sciences, 2022. p. 1–228.
   Google Scholar
• Rehman A, Naz S, Razzak MI, et al. A deep learning-based framework for automatic brain tumors classification using transfer learning. Circ Syst Signal Process. 2020;39(2):757–775.
   Web of Science ®Google Scholar
• Gopal A. Hybrid classifier: brain tumor classification and segmentation using genetic-based grey wolf optimization. Multimed Res. 2020;3:2.
   Google Scholar
• Gokulkumari G. Classification of brain tumor using Manta Ray foraging optimization-based DeepCNN classifier. Multimed Res. 2020;3:4.
   Google Scholar
• Rehman A, Khan MA, Saba T, et al. Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture. Microsc Res Tech. 2021;84(1):133–149.
   PubMed Web of Science ®Google Scholar
• Rajinikanth V, Joseph Raj AN, Thanaraj KP, et al. A customized VGG19 network with concatenation of deep and handcrafted features for brain tumor detection. Appl Sci. 2020;10(10):3429.
   Google Scholar
• Srinivas C, Prasad N, Zakariah M, et al. Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images. J Healthc Eng. 2022;2:1–17.
   Google Scholar
• Sharif M, Amin J, Raza M, et al. Brain tumor detection based on extreme learning. Neural Comp Appl. 2020;32(20):15975–15987.
   Web of Science ®Google Scholar
• Deb D, Roy S. Brain tumor detection based on hybrid deep neural network in MRI by adaptive squirrel search optimization. Multimed Tools Appl. 2021;80(2):2621–2645.
   Web of Science ®Google Scholar
• Amin J, Sharif M, Raza M, et al. Brain tumor detection: a long short-term memory (LSTM)-based learning model. Neural Comp Appl. 2020;32(20):15965–15973.
   Web of Science ®Google Scholar
• Sharma AK, Nandal A, Dhaka A, et al. Enhanced watershed segmentation algorithm-based modified ResNet50 model for brain tumor detection. BioMed Res Int. 2022;1:1–14.
   Web of Science ®Google Scholar
• Bilenia A, Sharma D, Raj H, et al. Brain tumor segmentation with skull stripping and modified fuzzy C-means. In: Information and communication technology for intelligent systems. Singapore: Springer; 2019. p. 106: 229–237.
   Google Scholar
• Wang S, Yi L, Chen Q, et al. Edge-aware fully convolutional network with CRF-RNN layer for hippocampus segmentation. In: The proceedings of 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2019. p. 803–806.
   Google Scholar
• Zhao W, Wang L, Mirjalili S. Artificial hummingbird algorithm: a new bio-inspired optimizer with its engineering applications. Comput Methods Appl Mech Eng. 2022;388:114194.
   Web of Science ®Google Scholar
• Su R, Liu T, Sun C, et al. Fusing convolutional neural network features with hand-crafted features for osteoporosis diagnoses. Neurocomputing. 2020;385:300–309.
   Web of Science ®Google Scholar
• Iqbal N, Mumtaz R, Shafi U, et al. Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms. PeerJ Comp Sci. 2021;7:e536.
   PubMedGoogle Scholar
• Fausto F, Cuevas E, Gonzales A. A new descriptor for image matching based on bionic principles. Pattern Anal Appl. 2017;20(4):1245–1259.
   Web of Science ®Google Scholar
• Bai Y, Guo L, Jin L, et al. A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition. In: The proceedings of IEEE 16th International Conference on Image Processing (ICIP), November 2009. p. 3305–3308.
   Google Scholar
• Fan KC, Hung TY. A novel local pattern descriptor – local vector pattern in high-order derivative space for face recognition. IEEE Trans Image Process. 2014;23(7):2877–2891.
   PubMed Web of Science ®Google Scholar
• Sarraf S, Tofighi G. Classification of Alzheimer’s disease using FMRI data and deep learning convolutional neural networks. arXiv preprint arXiv:1603.08631, 2016.
   Google Scholar
• Abdollahzadeh B, Gharehchopogh FS, Mirjalili S. African vultures optimization algorithm: a new nature-inspired metaheuristic algorithm for global optimization problems. Comput Ind Eng. 2021;158:107408.
   Web of Science ®Google Scholar
• Zheng S, Jayasumana S, Romera-Paredes B, et al. Conditional random fields as recurrent neural networks. In Proceedings of the IEEE international conference on computer vision, 2015. p. 1529–1537.
   Google Scholar
• Figshare dataset. [cited 2022 Sep] available at https://figshare.com/articles/brain_tumor_dataset/1512427.
   Google Scholar
• BRATS 2018 database. [cited 2022 Sep] available at https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=37224922.
   Google Scholar
• Renjit A. Deepjoint segmentation for the classification of severity-levels of glioma tumour using multimodal MRI images. IET Image Proc. 2020;14(11):2541–2552.
   Web of Science ®Google Scholar
• Badrinarayanan V, Kendall A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(12):2481–2495.
   PubMed Web of Science ®Google Scholar
• Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Proceedings of international conference on medical image computing and computer-assisted intervention. Cham: Springer; 2015. p. 234–241.
   Google Scholar