{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T10:22:08Z","timestamp":1783851728933,"version":"3.55.0"},"reference-count":50,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020R1G1A1012741"],"award-info":[{"award-number":["NRF-2020R1G1A1012741"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.<\/jats:p>","DOI":"10.3390\/s22072726","type":"journal-article","created":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T21:23:55Z","timestamp":1648848235000},"page":"2726","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":141,"title":["A New Deep Hybrid Boosted and Ensemble Learning-Based Brain Tumor Analysis Using MRI"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7345-4834","authenticated-orcid":false,"given":"Mirza Mumtaz","family":"Zahoor","sequence":"first","affiliation":[{"name":"Department of Computer & Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 45650, Pakistan"},{"name":"Pattern Recognition Lab, (DCIS), PIEAS, Islamabad 45650, Pakistan"},{"name":"Faculty of Computer Science, Ibadat International University, Islamabad 54590, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8213-1431","authenticated-orcid":false,"given":"Shahzad Ahmad","family":"Qureshi","sequence":"additional","affiliation":[{"name":"Department of Computer & Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 45650, Pakistan"},{"name":"Pattern Recognition Lab, (DCIS), PIEAS, Islamabad 45650, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sameena","family":"Bibi","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Air University, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6681-1987","authenticated-orcid":false,"given":"Saddam Hussain","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer & Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 45650, Pakistan"},{"name":"Pattern Recognition Lab, (DCIS), PIEAS, Islamabad 45650, Pakistan"},{"name":"Department of Computer System Engineering, University of Engineering and Applied Science (UEAS), Swat 19060, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2039-5305","authenticated-orcid":false,"given":"Asifullah","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer & Information Sciences (DCIS), Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 45650, Pakistan"},{"name":"Pattern Recognition Lab, (DCIS), PIEAS, Islamabad 45650, Pakistan"},{"name":"PIEAS Artificial Intelligence Center (PAIC), PIEAS, Islamabad 45650, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5464-4002","authenticated-orcid":false,"given":"Usman","family":"Ghafoor","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Institute of Space Technology, Islamabad 44000, Pakistan"},{"name":"School of Mechanical Engineering, Pusan National University, Busan 46241, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3213-7154","authenticated-orcid":false,"given":"Muhammad Raheel","family":"Bhutta","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/S0140-6736(03)12328-8","article-title":"Primary brain tumours in adults","volume":"361","author":"Carpentier","year":"2003","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s00401-016-1545-1","article-title":"The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A summary","volume":"131","author":"Louis","year":"2016","journal-title":"Acta Neuropathol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bengio, Y. (2009). Learning Deep Architectures for AI, Now Publishers Inc.. Foundations and Trends\u00ae in Machine Learning.","DOI":"10.1561\/9781601982957"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, S., Muhammad, K., Phillips, P., Dong, Z., and Zhang, Y.-D. (2017). Ductal carcinoma in situ detection in breast thermography by extreme learning machine and combination of statistical measure and fractal dimension. J. Ambient Intell. Humaniz. Comput., 1\u201311.","DOI":"10.1007\/s12652-017-0639-5"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Codella, N.C.F., Gutman, D., Celebi, M.E., Helba, B., Marchetti, M.A., Dusza, S.W., Kalloo, A., Liopyris, K., Mishra, N., and Kittler, H. (2018, January 4\u20137). Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (ISBI), Hosted by the international skin imaging collaboration (ISIC). Proceedings of the IEEE 15th International Symposium on Biomedical Imaging (ISBI), Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Khan, S.H., Khan, A., Lee, Y.S., and Hassan, M. (2021). Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder. arXiv.","DOI":"10.1007\/s11042-022-14061-x"},{"key":"ref_7","unstructured":"Khan, S.H., Sohail, A., Khan, A., and Lee, Y.S. (2020). Classification and region analysis of COVID-19 infection using lung CT images and deep convolutional neural networks. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102676","DOI":"10.1016\/j.pdpdt.2021.102676","article-title":"Detection of tumour infiltrating lymphocytes in CD3 and CD8 stained histopathological images using a two-phase deep CNN","volume":"37","author":"Zafar","year":"2021","journal-title":"Photodiagn. Photodyn. Ther."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Khan, A., Khan, S.H., Saif, M., Batool, A., Sohail, A., and Khan, M.W. (2022). A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron. arXiv.","DOI":"10.1080\/0952813X.2023.2165724"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","article-title":"Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions","volume":"30","author":"Akkus","year":"2017","journal-title":"J. Digit. Imaging"},{"key":"ref_12","first-page":"71","article-title":"A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine","volume":"17","author":"Kharrat","year":"2010","journal-title":"Leonardo J. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0304-3835(97)00233-4","article-title":"Neural networks analysis of astrocytic gliomas from MRI appearances","volume":"118","author":"Abdolmaleki","year":"1997","journal-title":"Cancer Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1016\/j.asoc.2007.06.006","article-title":"Brain tumor characterization using the soft computing technique of fuzzy cognitive maps","volume":"8","author":"Papageorgiou","year":"2008","journal-title":"Appl. Soft Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1002\/mrm.22147","article-title":"Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme","volume":"62","author":"Zacharaki","year":"2009","journal-title":"Magn. Reson. Med. Off. J. Int. Soc. Magn. Reson. Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Ashraf, I., Alhaisoni, M., Dama\u0161evi\u010dius, R., Scherer, R., Rehman, A., and Bukhari, S.A.C. (2020). Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists. Diagnostics, 10.","DOI":"10.3390\/diagnostics10080565"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Maqsood, S., Damasevicius, R., and Shah, F.M. (2021). An Efficient Approach for the Detection of Brain Tumor Using Fuzzy Logic and U-NET CNN Classification. Computational Science and Its Applications\u2014ICCSA, Springer International Publish.","DOI":"10.1007\/978-3-030-86976-2_8"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1007\/s12065-020-00539-w","article-title":"Evaluation of brain tumor using brain MRI with modified-moth-flame algorithm and Kapur\u2019s thresholding: A study","volume":"14","author":"Kadry","year":"2021","journal-title":"Evol. Intell."},{"key":"ref_19","unstructured":"Jun, C. (2022, January 22). Brain Tumor Dataset %U. Available online: https:\/\/figshare.com\/articles\/brain_tumor_dataset\/1512427."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., and Feng, Q. (2015). Correction: Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0144479"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"69215","DOI":"10.1109\/ACCESS.2019.2919122","article-title":"Multi-Classification of Brain Tumor Images Using Deep Neural Network","volume":"7","author":"Sultan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"109684","DOI":"10.1016\/j.mehy.2020.109684","article-title":"Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture","volume":"139","author":"Yildirim","year":"2020","journal-title":"Med. Hypotheses"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Khawaldeh, S., Pervaiz, U., Rafiq, A., and Alkhawaldeh, R.S. (2017). Noninvasive Grading of Glioma Tumor Using Magnetic Resonance Imaging with Convolutional Neural Networks. Appl. Sci., 8.","DOI":"10.3390\/app8010027"},{"key":"ref_24","unstructured":"Perez, L., and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_27","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going Deeper with Convolutions. arXiv.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2017). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. arXiv.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","article-title":"Artificial neural networks (the multilayer perceptron)\u2014A review of applications in the atmospheric sciences","volume":"32","author":"Gardner","year":"1998","journal-title":"Atmos. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Schapire, R.E. (2013). Explaining Adaboost. Empirical Inference, Springer.","DOI":"10.1007\/978-3-642-41136-6_5"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"104816","DOI":"10.1016\/j.compbiomed.2021.104816","article-title":"COVID-19 detection in chest X-ray images using deep boosted hybrid learning","volume":"137","author":"Khan","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Khan, S.H., Sohail, A., Khan, A., and Lee, Y.-S. (2020). COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN. arXiv.","DOI":"10.1016\/j.compbiomed.2021.104816"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"102473","DOI":"10.1016\/j.pdpdt.2021.102473","article-title":"Coronavirus Disease Analysis using Chest X-ray Images and a Novel Deep Convolutional Neural Network","volume":"35","author":"Khan","year":"2021","journal-title":"Photodiagn. Photodyn. Ther."},{"key":"ref_40","unstructured":"Chakrabarty, N. (2022, January 22). Brain MRI Images for Brain Tumor Detection. Available online: https:\/\/www.kaggle.com\/datasets\/navoneel\/brain-mri-images-for-brain-tumor-detection."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2012). Stochastic gradient descent tricks. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8_25"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1198\/073500102753410444","article-title":"Comparing predictive accuracy","volume":"20","author":"Diebold","year":"2002","journal-title":"J. Bus. Econ. Stat."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1002\/(SICI)1097-4571(199401)45:1<12::AID-ASI2>3.0.CO;2-L","article-title":"The relationship between recall and precision","volume":"45","author":"Buckland","year":"1994","journal-title":"J. Am. Soc. Inf. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sokolova, M., Japkowicz, N., and Szpakowicz, S. (2006). Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation. Australasian Joint Conference on Artificial Intelligence, Springer.","DOI":"10.1007\/11941439_114"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Boughorbel, S., Jarray, F., and El Anbari, M. (2017). Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0177678"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Davis, J., and Goadrich, M. (2006, January 25\u201329). The Relationship Between Precision-Recall and ROC Curves. Proceedings of the 23rd International Conference on Machine Learning, ACM, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143874"},{"key":"ref_47","first-page":"84","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bad\u017ea, M.M., and Barjaktarovi\u0107, M. (2020). Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network. Appl. Sci., 10.","DOI":"10.3390\/app10061999"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"36266","DOI":"10.1109\/ACCESS.2019.2904145","article-title":"A Hybrid Feature Extraction Method with Regularized Extreme Learning Machine for Brain Tumor Classification","volume":"7","author":"Gumaei","year":"2019","journal-title":"IEEE Access"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"D\u00edaz-Pernas, F.J., Mart\u00ednez-Zarzuela, M., Ant\u00f3n-Rodr\u00edguez, M., and Gonz\u00e1lez-Ortega, D. (2021). A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network. Healthcare, 9.","DOI":"10.3390\/healthcare9020153"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2726\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:48:42Z","timestamp":1760136522000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2726"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,1]]},"references-count":50,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22072726"],"URL":"https:\/\/doi.org\/10.3390\/s22072726","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,1]]}}}