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Enhanced Performance of Brain Tumor Classification via Tumor Region Augmentation and Partition
Open accessAbstract
Automatic classification of tissue types of region of interest (ROI) plays an important role in computer-aided diagnosis. In the current study, we focus on the classification of three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor) in T1-weighted contrast-enhanced MRI (CE-MRI) images. Spatial pyramid matching (SPM), which splits the image into increasingly fine rectangular subregions and computes histograms of local features from each subregion, exhibits excellent results for natural scene classification. However, this approach is not applicable for brain tumors, because of the great variations in tumor shape and size. In this paper, we propose a method to enhance the classification performance. First, the augmented tumor region via image dilation is used as the ROI instead of the original tumor region because tumor surrounding tissues can also offer important clues for tumor types. Second, the augmented tumor region is split into increasingly fine ring-form subregions. We evaluate the efficacy of the proposed method on a large dataset with three feature extraction methods, namely, intensity histogram, gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model. Compared with using tumor region as ROI, using augmented tumor region as ROI improves the accuracies to 82.31% from 71.39%, 84.75% from 78.18%, and 88.19% from 83.54% for intensity histogram, GLCM, and BoW model, respectively. In addition to region augmentation, ring-form partition can further improve the accuracies up to 87.54%, 89.72%, and 91.28%. These experimental results demonstrate that the proposed method is feasible and effective for the classification of brain tumors in T1-weighted CE-MRI.
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“Data collected from three different kinds of brain tumors consisted of 708 meningiomas, 1426 gliomas, and 930 pituitaries images from the General Hospital and Nanfang Hospital, Tianjin Medical University, China [72].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [72]
In the citing paper
“This contains 3064 T1-weighted contrast-enhanced images collected from 233 patients with a resolution of 512 × 512 pixels.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [72]
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Cites
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“We also compared three classification methods (i.e. SVM, sparse representation-based classification (SRC) [31], and k nearest neighbors (kNN) classifier) using 2-region BoW representations.”
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“Among those methods, the most notable work is SPM [15], which splits the image into hierarchical cells, computes BoW representation for each cell, and finally weights and concatenates the results.”
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Gunasekara, S. R., Kaldera, H. N. T. K., & Dissanayake, M. B. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/6695108 · Cited by 98
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BackgroundMethodInfluentialBrain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint
Gu, X., Shen, Z., Xue, J., Fan, Y., & Ni, T. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.679847 · Cited by 96
“The brain tumor datasets used in this article are provided by Cheng et al. (2015, 2016) and the Repository of Molecular Brain Neoplasia Data (REMBRANDT) (Clark et al., 2013).”
MethodKhan, S. U. R., Zhao, M., Asif, S., & Chen, X. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22975 · Cited by 96
“On the Figshare dataset, 31 – 33 all previous efforts have been evaluated for their ability to distinguish between glioma, meningioma, and pituitary tumors.”
BackgroundA Comprehensive Survey of Foundation Models in Medicine
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RetractedAutomated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning
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“In 2015, Cheng developed a figshare dataset of brain tumors [34], and furtherutilized it [30] to attempt the three classes problem of detecting brain malignancies.”
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“Every patient has four modalities of the MR images (T1, T2, T1CE, and FLAIR).”
BackgroundInfluentialBrain tumor Classification and Segmentation using Faster R-CNN
Kaldera, H. N. T. K., Gunasekara, S. R., & Dissanayake, M. B. (2019) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/icaset.2019.8714263 · Cited by 86
“In this research, T1-weighed MRI brain tumor dataset presented at [14], [15] is used.”
MethodRani, S., Ghai, D., Kumar, S., Kantipudi, M. P., Alharbi, A. H., & Ullah, M. A. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7882924 · Cited by 88
“[50] Momina Masood et al.[50] Proposed Method Outcomes on Figshare Dataset”
BackgroundSystematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis
Kim, J. W., Khan, A. U., & Banerjee, I. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01322-4 · Cited by 99
“Following the similar trend as CNN, hybrid models are often trained and validated on open-source datasets, e.g., BraTS [32], Figshare [33], KiTS21 [34], TCIA [35], Synapse, with limited samples of high dimensional medical images, and obtained current state-of-the-art performance for all major…”
Explainable ensemble deep learning-based model for brain tumor detection and classification
Hosny, K. M., Mohammed, M., Salama, R. A., & Elshewey, A. M. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10401-0 · Cited by 96
“In this work, we used the contrast-enhanced MRI dataset initially used in [33, 34] and publicly available on Figshare [20], which includes 3064 T1-weighted contrast-enhanced images obtained from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University, China, from 2005…”
Qureshi, S. A., Hussain, L., Ibrar, U., Alabdulkreem, E. A., Nour, M. K., Alqahtani, M. S., Nafie, F. M., Mohamed, A., Mohammed, G. P., & Duong, T. Q. (2023) · Scientific Reports · doi:10.1038/s41598-023-30309-4 · Cited by 91
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TumorDetNet: A unified deep learning model for brain tumor detection and classification
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Classification of Brain MRI Tumor Images Based on Deep Learning PGGAN Augmentation
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“…of our framework with its three proposed models (VGG19 + CNN, VGG19 + GRU, and VGG19 + Bi-GRU) with eight recent state-of-the-art models [4,23,30–32, 34, We first compared the results of studies of four state-of-the-art models that used classical augmentation [4,23,31,38] with the results of…”
BackgroundMethodResultInfluentialA hybrid feature extraction approach for brain MRI classification based on Bag-of-words
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Self-citationMRI brain tumor medical images analysis using deep learning techniques: a systematic review
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BTC-fCNN: Fast Convolution Neural Network for Multi-class Brain Tumor Classification
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“[7] implemented brain tumor classification for glioma, pituitary, and meningioma, using Gray level co-occurrence matrix (GLCM), and bag-of-words (BoW) model.”
BackgroundMethodHaq, A. U., Li, J., Agbley, B. L. Y., Khan, A., Khan, I. U., Uddin, M. I., & Khan, S. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3171663 · Cited by 81
“A new version was published in 2017 [11].”
BackgroundMethodBrain Tumor Identification Using Data Augmentation and Transfer Learning Approach
Kumar, K. K., Dinesh, P. M., Rayavel, P., Vijayaraja, L., Dhanasekar, R., Kesavan, R., Kannadasan, R., Khan, A. A., Wechtaisong, C., Haq, M. A., et al. (2023) · Computer Systems Science and Engineering · doi:10.32604/csse.2023.033927 · Cited by 85
Brain Tumor Classification Based on Attention Guided Deep Learning Model
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“The experiment was carried out on the brain tumor dataset figshare [3, 4].”
MethodMBTFCN: A novel modular fully convolutional network for MRI brain tumor multi-classification
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Multimodal brain tumor detection using multimodal deep transfer learning
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RetractedAn automated deep learning framework for brain tumor classification using MRI imagery
Aamir, M., Rahman, Z., Bhatti, U. A., Abro, W. A., Bhutto, J. A., & He, Z. (2025) · Scientific Reports · doi:10.1038/s41598-025-02209-2 · Cited by 70
“The effectiveness of the proposed model was assessed using the publicly accessible brain tumor dataset that was initially provided by Cheng et al. 30 .”
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Self-citationBrain tumor segmentation and classification on MRI via deep hybrid representation learning
Farajzadeh, N., Sadeghzadeh, N., & Hashemzadeh, M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119963 · Cited by 77
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MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system
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“In this study, we have used a brain tumor data set from Nanfang hospital and General hospital, Tianjing medical university, of China (Cheng et al. 2015), and new versions in 2017 have been published.”
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Efficient Brain Tumor Detection with Lightweight End-to-End Deep Learning Model
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“Our research utilizes a specialized dataset from Kaggle, sourced from the T1-CE MRI collection, which includes 3064 brain MRI images [52] essential for training our tumor segmentation model.”
Brain Tumor: Hybrid Feature Extraction Based on UNet and 3DCNN
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“Fatih Demir et al. (2022) [24] brain tumors pose a global threat, and Magnetic Resonance Imaging (MRI) is a widely used diagnostic tool.”
BackgroundEffect of Data Pre-processing on Brain Tumor Classification Using Capsulenet
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“The brain tumor dataset 49,50 consisted of 3064 T1-weighted contrast enhanced images with three kinds of brain tumors from 233 patients, including gliomas (1426 slices), meningiomas (708 slices) and pituitary tumors (930 slices).”
BackgroundThe Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey
Shirazi, A. Z., Fornaciari, E., McDonnell, M. D., Yaghoobi, M., Cevallos, Y., Tello-Oquendo, L., Inca, D., & Gomez, G. A. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10040224 · Cited by 66
“[26] ** 2020 C lassification – Glioma – Meningioma – Pituitary – Glioblastoma MRI Six DCNNArchitectures * A prospective survey on Deep Learning techniques applied for Multigrade Brain Tumor Classification – Caffe – NVidia DIGITS NVidia TITAN X (Pascal) – multigrade brain tumor [51] – Brain tumor public data Set [52] –…”
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Brain Tumor Detection and Classification Using an Optimized Convolutional Neural Network
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“Glioma, meningioma, and pituitary tumors stand out as separate forms of brain tumors [2].”
BackgroundBrain Tumor Classification Using Deep Learning
Waghmare, V. K., & Kolekar, M. H. (2020) · Studies in big data · doi:10.1007/978-981-15-4112-4_8 · Cited by 59
MedIAnomaly: A comparative study of anomaly detection in medical images
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Brain tumor detection and segmentation using deep learning
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“The repository provides a total of 3064 slices for 233 subjects (in-plane resolution = 512 × 512, pixel size = 0.49 × 0.49 mm 2 , slice thickness = 6 mm, slice gap = 1 mm) [4] and the corresponding tumor segmentation binary masks.”
InfluentialBMRI-NET: A Deep Stacked Ensemble Model for Multi-class Brain Tumor Classification from MRI Images
Asif, S., Zhao, M., Chen, X., & Zhu, Y. (2023) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-023-00571-1 · Cited by 56
Dual Deep CNN for Tumor Brain Classification
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Smart brain tumor diagnosis system utilizing deep convolutional neural networks
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“In this survey, the accuracy rates of the two models generated by [61] and [15] based on VGGNet were 93% and 94%, respectively.”
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Neural memory plasticity for medical anomaly detection
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“…classes, static scenes, annotations ’ limitations, static object views, and potential lack of semantic context and temporal variation In a study ( Cheng et al., 2015 ), the Figshare dataset explored an alternative algorithm for enhancing tumor regions as areas of interest, which were…”
BackgroundComputationally optimized brain tumor classification using attention based GoogLeNet-style CNN
Subba, A. B., & Sunaniya, A. K. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125443 · Cited by 51
Capsule Network with Its Limitation, Modification, and Applications—A Survey
Haq, M. U., Sethi, M. A. J., & Rehman, A. U. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5030047 · Cited by 54
Brain tumor localization and segmentation using mask RCNN
Masood, M., Nazir, T., Nawaz, M., Javed, A., Iqbal, M., & Mehmood, A. (2021) · Frontiers of Computer Science · doi:10.1007/s11704-020-0105-y · Cited by 48
An automated brain tumor classification in MR images using an enhanced convolutional neural network
Singh, R., & Agarwal, B. B. (2022) · International Journal of Information Technology · doi:10.1007/s41870-022-01095-5 · Cited by 50
“To categorize each patch of a brain tumour, the above-mentioned techniques created utilizing CNNs for brain tumour classification consider local areas in MRI images [16].”
MethodMachine learning based multipurpose medical image watermarking
Sinhal, R., & Ansari, I. A. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08457-5 · Cited by 48
Ali, M. U., Hussain, S. J., Zafar, A., Bhutta, M. R., & Lee, S. W. (2023) · Bioengineering · doi:10.3390/bioengineering10040475 · Cited by 47
“A total of 3064 brain MRI scans were obtained at two hospitals in China (Nanfang Hospital and General Hospital), of which 1426 images comprised glioma tumors, 708 comprised meningioma tumors, and 930 comprised pituitary gland tumors [64].”
Ullah, M. S., Khan, M. A., Albarakati, H. M., Damaševičius, R., & Alsenan, S. A. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109183 · Cited by 47
Deepika, J., Rajan, C., & Senthil, T. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/6615411 · Cited by 52
A novel adaptive momentum method for medical image classification using convolutional neural network
Aytaç, U. C., Güneş, A., & Ajlouni, N. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00755-z · Cited by 50
“For example, the author [12] has introduced brain tumor classification using CNN’s while still some researchers using support vector machines [13], region augmentation [15, 19], wavelet transformations [16, 17].”
MethodInterpretable features fusion with precision MRI images deep hashing for brain tumor detection
Özbay, E., & Ozbay, F. A. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107387 · Cited by 46
A secure hybrid deep learning framework for brain tumor detection and classification
Mathivanan, S. K., Srinivasan, S., Koti, M. S., Kushwah, V. S., Joseph, R. B., & Shah, M. A. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01117-6 · Cited by 46
“Unlike prior works, such as CNN architectures with transfer learning by Arshia Rehman et al. [18] or voting classifiers by Nazik Alturki et al. [28], which are built using certain datasets and struggle with applicability, the BTDN has an incredible precision of 99.68% on different heterogeneous…”
InfluentialA fast technique for image segmentation based on two Meta-heuristic algorithms
Chouksey, M., Jha, R. K., & Sharma, R. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08138-3 · Cited by 47
“Different MRI T1-weighted contrast enhanced images are chosen for test images [7].”
BackgroundIncremental Dilations Using CNN for Brain Tumor Classification
Roy, S. S., Rodrigues, N., & Taguchi, Y. (2020) · Applied Sciences · doi:10.3390/app10144915 · Cited by 46
MSG-GAN Based Synthesis of Brain MRI with Meningioma for Data Augmentation
Deepak, S., & Ameer, P. M. (2020) · IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT) · doi:10.1109/conecct50063.2020.9198672 · Cited by 46
“The first work on this research problem used the bag of words (BoW) as image features and a support vector machine classifier (SVM) [16].”
MethodIntra-domain task-adaptive transfer learning to determine acute ischemic stroke onset time
Zhang, H., Polson, J., Nael, K., Salamon, N., Yoo, B. Y., El-Saden, S. M., Scalzo, F., Speier, W. F., & Arnold, C. W. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101926 · Cited by 46
A computer-aided grading of glioma tumor using deep residual networks fusion
Tripathi, P. C., & Bag, S. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106597 · Cited by 47
Clever Hans effect found in a widely used brain tumour MRI dataset
Wallis, D., & Buvat, I. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102368 · Cited by 48
BoostCaps: A Boosted Capsule Network for Brain Tumor Classification
Afshar, P., Plataniotis, K. N., & Mohammadi, A. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175922 · Cited by 45
“2, we used a brain cancer dataset [21], consisting of 3064 brain MRI images from 233 patients, where 20% of the data is set aside for testing purposes.”
MethodG-ResNet: Improved ResNet for Brain Tumor Classification
Liu, D., Liu, Y., & Dong, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-36708-4_44 · Cited by 46
Shah, S. M. A. H., Ullah, A., Iqbal, J., Bourouis, S., Ullah, S. S., Hussain, S., Khan, M. Q., Shah, Y. A., & Mustafa, G. (2023) · IEEE Access · doi:10.1109/access.2023.3294562 · Cited by 50
Mohammed, B. A., Senan, E. M., Alshammari, T. A., Alreshidi, A., Alayba, A. M., Alazmi, M., & Alsagri, A. N. E. A. (2023) · Processes · doi:10.3390/pr11010212 · Cited by 43
Enhancing brain tumor classification through ensemble attention mechanism
CELIK, F., CELIK, K., & Celik, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-73803-z · Cited by 43
“Brain tumor classification was given a new twist by Cheng et al. 24 , who focused on enhancing performance by augmenting the tumor region.”
BackgroundDeep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets
Kadhim, Y. A., Khan, M. U., & Mishra, A. (2022) · Sensors · doi:10.3390/s22228999 · Cited by 43
“Cheng et al. [59] used the Bag of Words (BoW) to extract the features from the images and SVM are used in the classification of the images.”
MethodInfluentialModified U-Net with attention gate for enhanced automated brain tumor segmentation
Saifullah, S., Dreżewski, R., Yudhana, A., Wielgosz, M., & Caesarendra, W. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10919-3 · Cited by 42
Hassan, N. M. H., & Boulila, W. (2025) · IEEE Access · doi:10.1109/access.2025.3566332 · Cited by 42
“Brain cancers vary widely in terms of their density levels, shapes, and dimensions [7].”
Multi-level Dense Capsule Networks
Phaye, S. S. R., Sikka, A., Dhall, A., & Bathula, D. R. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20873-8_37 · Cited by 42
Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study
Tang, T. T., Zawaski, J. A., Francis, K. N., Qutub, A. A., & Gaber, M. W. (2019) · Scientific Reports · doi:10.1038/s41598-019-48738-5 · Cited by 42
An Efficient Method to Classify Brain Tumor using CNN and SVM
Sejuti, Z. A., & Islam, M. S. (2021) · International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST) · doi:10.1109/icrest51555.2021.9331060 · Cited by 42
“11 (2012): 6929-6942 [13] Cheng, Jun, et al.”
Gencer, K., & Gencer, G. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.2556 · Cited by 49
Deep learning guided by an ontology for medical images classification using a multimodal fusion
Yahyaoui, H., Ghazouani, F., & Farah, I. R. (2021) · International Congress of Advanced Technology and Engineering (ICOTEN) · doi:10.1109/icoten52080.2021.9493469 · Cited by 41
Crossover based technique for data augmentation
Raj, R., Mathew, J., Kannath, S. K., & Rajan, J. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106716 · Cited by 39
Saha, A., Tso, S., Rabski, J., Sadeghian, A., & Cusimano, M. D. (2020) · Pituitary · doi:10.1007/s11102-019-01026-x · Cited by 41
“In comparison to other brain diseases such as GBM [78] and Alzheimer’s [79, 80], public availability of labelled pituitary tumor imaging datasets is rare (an exception is slice-wise labeled meningioma, glioma, and pituitary brain tumor ROIs in dataset provided by Cheng et al. [81]) .”
BackgroundMSENet: Multi-Modal Squeeze-and-Excitation Network for Brain Tumor Severity Prediction
Bodapati, J. D., Shaik, N. S., Veeranjaneyulu, N., & Mundukur, N. B. (2021) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001421570056 · Cited by 40
Xu, C., Zhang, T., Zhang, D., Zhang, D., & Han, J. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3383716 · Cited by 39
“E XPERIMENT A. Data Acquirement 1) Brain Tumor Dataset [25], [44]: The brain tumor dataset was acquired from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjing Medical University, China, from 2005 to 2010.”
Method
Metadata
- Authors
- Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., & Feng, Q.
- Affiliations
- Southern Medical University (CN)
- Nanfang Hospital (CN)
- Corresponding author
- Feng, Qianjin
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-10-08
- Volume
- 10
- Issue
- 10
- Pages
- e0140381
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4598126
- OpenAlex
- W2182098131
- Cited by
- 879
- References
- 36
- Citations by year
- 2026: 110 · 2025: 134 · 2024: 142 · 2023: 152 · 2022: 128 · 2021: 85 · 2020: 63 · 2019: 44 · 2018: 14 · 2017: 4 · 2016: 2 · 2015: 1
- FWCI
- 3.74
- Type
- journal article
- Language
- en
- Update notices
- correction (2015-12-02)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Natural Science Foundation of China (81101109, 31371009, 2012BAI14B02, 863 Program)
- National High-tech Research and Development Program
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- brain tumor classification, meningioma, glioma, pituitary tumors, region of interest, image dilation, spatial pyramid matching, contrast-enhanced T1-weighted imaging, computer-aided diagnosis, gray level co-occurrence matrix, bag-of-words model, intensity histogram
- MeSH terms
- Brain Neoplasms, Glioma, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Meningioma, Sensitivity and Specificity
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