A Modified Deep Convolutional Neural Network for Abnormal Brain Image Classification

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals

Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.09.017 · Cited by 1,720

Classification using deep learning neural networks for brain tumors

Mohsen, H., El‐Dahshan, E. A., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2017) · Future Computing and Informatics Journal · doi:10.1016/j.fcij.2017.12.001 · Cited by 1,076

Deep Learning and Its Applications in Biomedicine

Cao, C., Liu, F., Tan, H., Song, D., Shu, W., Li, W., Zhou, Y., Bo, X., & Xie, Z. (2018) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2017.07.003 · Cited by 640

NiftyNet: a deep-learning platform for medical imaging

Gibson, E., Li, W., Sudre, C., Fidon, L., Shakir, D. I., Wang, G., Eaton-Rosen, Z., Gray, R., Doel, T., Hu, Y., et al. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.01.025 · Cited by 552

Medical image retrieval using deep convolutional neural network

Qayyum, A., Anwar, S. M., Awais, M., & Majid, M. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.05.025 · Cited by 433

Classification of CT brain images based on deep learning networks

Gao, X. W., Hui, R., & Tian, Z. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.10.007 · Cited by 366

Big data analysis for brain tumor detection: Deep convolutional neural networks

Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.04.065 · Cited by 327

Multi-Site Diagnostic Classification of Schizophrenia Using Discriminant Deep Learning with Functional Connectivity MRI

Zeng, L., Wang, H., Hu, P., Yang, B., Pu, W., Shen, H., Chen, X., Liu, Z., Yin, H. J., Tan, Q., et al. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.03.017 · Cited by 280

Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging

Choi, H., & Jin, K. H. (2018) · Behavioural Brain Research · doi:10.1016/j.bbr.2018.02.017 · Cited by 268

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Kang, J., Ullah, Z., & Gwak, J. (2021) · Sensors · doi:10.3390/s21062222 · Cited by 641

A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images

Khairandish, M. O., Sharma, M., Jain, V. K., Chatterjee, J. M., & Jhanjhi, N. Z. (2021) · IRBM · doi:10.1016/j.irbm.2021.06.003 · Cited by 549

Deep CNN for Brain Tumor Classification

Elhamzi, W., Elhamzi, W., Charfi, I., & Atri, M. (2021) · Neural Processing Letters · doi:10.1007/s11063-020-10398-2 · Cited by 481

An enhanced deep learning approach for brain cancer MRI images classification using residual networks

Ismael, S. A. A., Mohammed, A., & Hefny, H. A. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101779 · Cited by 443

Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM

Le, T., Vo, M. T., Vo, B., Hwang, E., Rho, S., & Baik, S. W. (2019) · Applied Sciences · doi:10.3390/app9204237 · Cited by 267

A Novel Framework for Trash Classification Using Deep Transfer Learning

Vo, A. H., Son, L. H., Vo, M. T., & Le, T. (2019) · IEEE Access · doi:10.1109/access.2019.2959033 · Cited by 222

Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network

Son, L. H., Kumar, A., Sangwan, S. R., Arora, A., Nayyar, A., & Abdel-Basset, M. (2019) · IEEE Access · doi:10.1109/access.2019.2899260 · Cited by 206

A classification of MRI brain tumor based on two stage feature level ensemble of deep CNN models

Aurna, N. F., Yousuf, M. A., Taher, K. A., Azad, A., & Moni, M. A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105539 · Cited by 212

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Lahoura, V., Singh, H., Aggarwal, A., Sharma, B., Mohammed, M. A., Damaševičius, R., Kadry, S., & Cengiz, K. (2021) · Diagnostics · doi:10.3390/diagnostics11020241 · Cited by 197

Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

Kader, I. A. E., Xu, G., Zhang, S., Saminu, S., Javaid, I., & Ahmad, I. S. (2021) · Brain Sciences · doi:10.3390/brainsci11030352 · Cited by 198

TimeDistributed-CNN-LSTM: A Hybrid Approach Combining CNN and LSTM to Classify Brain Tumor on 3D MRI Scans Performing Ablation Study

Montaha, S., Azam, S., Rafid, A. K. M. R. H., Hasan, M. Z., Karim, A., & Islam, A. (2022) · IEEE Access · doi:10.1109/access.2022.3179577 · Cited by 174

An enhanced deep learning method for multi-class brain tumor classification using deep transfer learning

Asif, S., Zhao, M., Tang, F., & Zhu, Y. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14828-w · Cited by 174

A Novel and Comprehensive Trust Estimation Clustering Based Approach for Large Scale Wireless Sensor Networks

Khan, T., Singh, K., Sơn, L. H., Abdel‐Basset, M., Long, H. V., SINGH, S. P., & Manjul, M. (2019) · IEEE Access · doi:10.1109/access.2019.2914769 · Cited by 128

Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review

Buchlak, Q. D., Esmaili, N., Leveque, J., Bennett, C. C., Farrokhi, F. R., & Piccardi, M. (2021) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2021.04.043 · Cited by 115

Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms

Rinesh, S., Maheswari, K., Arthi, B., Sherubha, P., Vijay, A., Sridhar, S., Rajendran, T., & Waji, Y. A. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/2761847 · Cited by 107

A Critical Review on Control Strategies for Structural Vibration Control

Wani, Z. R., Tantray, M., Farsangi, E. N., Nikitas, N., Noori, M. N., Samali, B., & Yang, T. Y. (2022) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2022.09.002 · Cited by 103

Attention transformer mechanism and fusion-based deep learning architecture for MRI brain tumor classification system

Tabatabaei, S., Rezaee, K., & Zhu, M. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105119 · Cited by 102

Medical images classification using deep learning: a survey

Kumar, R., Kumbharkar, P., Vanam, S., & Sharma, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15576-7 · Cited by 105

Multiple Electric Energy Consumption Forecasting Using a Cluster-Based Strategy for Transfer Learning in Smart Building

Le, T., Vo, M. T., Kieu, T., Hwang, E., Rho, S., & Baik, S. W. (2020) · Sensors · doi:10.3390/s20092668 · Cited by 89

Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model

Kader, I. A. E., Xu, G., Zhang, S., Saminu, S., Javaid, I., Ahmad, I. S., & Kamhi, S. (2021) · Diagnostics · doi:10.3390/diagnostics11091589 · Cited by 100

Deep Learning for Multi-Class Identification From Domestic Violence Online Posts

Subramani, S., Michalska, S., Wang, H., Du, J., Zhang, Y., & Shakeel, H. (2019) · IEEE Access · doi:10.1109/access.2019.2908827 · Cited by 88

An Effective and Novel Approach for Brain Tumor Classification Using AlexNet CNN Feature Extractor and Multiple Eminent Machine Learning Classifiers in MRIs

Sarkar, A., Maniruzzaman, M., Alahe, M. A., & Ahmad, M. (2023) · Journal of Sensors · doi:10.1155/2023/1224619 · Cited by 94

A MobileNets Convolutional Neural Network for GIS Partial Discharge Pattern Recognition in the Ubiquitous Power Internet of Things Context: Optimization, Comparison, and Application

Wang, Y., Yan, J., Sun, Q., Li, J., & Yang, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2946662 · Cited by 82

MBTFCN: A novel modular fully convolutional network for MRI brain tumor multi-classification

Shahin, A. I., Aly, W., & Aly, S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118776 · Cited by 77

RU-Net2+: A Deep Learning Algorithm for Accurate Brain Tumor Segmentation and Survival Rate Prediction

Zaitoon, R., & Syed, H. (2023) · IEEE Access · doi:10.1109/access.2023.3325294 · Cited by 77

A Deep Transfer Learning Based Architecture for Brain Tumor Classification Using MR Images

Badjie, B., & Ülker, E. D. (2022) · Information Technology And Control · doi:10.5755/j01.itc.51.2.30835 · Cited by 76

Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping

Zarenia, E., Far, A. A., & Rezaee, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-92776-1 · Cited by 68

Multi-Attribute Multi-Perception Decision-Making Based on Generalized T-Spherical Fuzzy Weighted Aggregation Operators on Neutrosophic Sets

Quek, S. G., Selvachandran, G., Munir, M., Mahmood, T., Ullah, K., Sơn, L. H., Thong, P. H., Kumar, R., & Priyadarshini, I. (2019) · Mathematics · doi:10.3390/math7090780 · Cited by 66

MRI Brain Tumor Image Classification Using a Combined Feature and Image-Based Classifier

Veeramuthu, A., Meenakshi, S., Mathivanan, G., Kotecha, K. V., Saini, J. R., Vijayakumar, V., & Subramaniyaswamy, V. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.848784 · Cited by 64

Detecting brain tumors using deep learning convolutional neural network with transfer learning approach

Anjum, S., Hussain, L., Ali, M., Alkinani, M. H., Aziz, W., Gheller, S., Abbasi, A. A., Marchal, A. R., Suresh, H., & Duong, T. Q. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22641 · Cited by 66

The 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

A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images

Fatema, K., Montaha, S., Rony, M. A. H., Azam, S., Hasan, M. Z., & Jonkman, M. (2022) · Biomedicines · doi:10.3390/biomedicines10112835 · Cited by 60

DCNNBT: A NOVEL DEEP CONVOLUTION NEURAL NETWORK-BASED BRAIN TUMOR CLASSIFICATION MODEL

Haq, M. A., Khan, I., Ahmed, A. U., Eldin, S. M., Alshehri, A., & Ghamry, N. A. (2023) · Fractals · doi:10.1142/s0218348x23401023 · Cited by 60

Probability-based cluster head selection and fuzzy multipath routing for prolonging lifetime of wireless sensor networks

Robinson, H., Julie, E. G., Kumar, R., & Sơn, L. H. (2019) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-019-00758-8 · Cited by 60

Deep Learning Techniques for the Classification of Brain Tumor: A Comprehensive Survey

Younis, A., Li, Q., Khalid, M., Clemence, B., & Adamu, M. J. (2023) · IEEE Access · doi:10.1109/access.2023.3317796 · Cited by 59

Computer vision classification of dry beans (Phaseolus vulgaris L.) based on deep transfer learning techniques

Taşpınar, Y. S., Doğan, M., Çınar, İ., Kursun, R., Özkan, İ. A., & Köklü, M. (2022) · European Food Research and Technology · doi:10.1007/s00217-022-04080-1 · Cited by 60

Brain structural disorders detection and classification approaches: a review

Bhatele, K. R., & Bhadauria, S. S. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09766-9 · Cited by 50

Incremental Dilations Using CNN for Brain Tumor Classification

Roy, S. S., Rodrigues, N., & Taguchi, Y. (2020) · Applied Sciences · doi:10.3390/app10144915 · Cited by 46

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

Dynamic interval valued neutrosophic set: Modeling decision making in dynamic environments

Thong, N. T., Dat, L. Q., Sơn, L. H., Hoa, N. Đ., Ali, M., & Smarandache, F. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.02.009 · Cited by 43

Brent Oil Price Prediction Using Bi-LSTM Network

Vo, A. H., Nguyen, T., & Le, T. (2020) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2020.013189 · Cited by 42

Ensemble learning based-features extraction for brain mr images classification with machine learning classifiers

Remzan, N., Hachimi, Y. E., Tahiry, K., & Farchi, A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17213-9 · Cited by 39

Machine learning-data mining integrated approach for premature ventricular contraction prediction

Mastoi, Q.-U.-A., Memon, M. S., Lakhan, A. R., Mohammed, M. A., Qabulio, M., Al-Turjman, F. M., & Abdulkareem, K. H. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05820-2 · Cited by 36

Machine learning and transfer learning techniques for accurate brain tumor classification

Malakouti, S. M., Menhaj, M. B., & Suratgar, A. A. (2024) · Clinical eHealth · doi:10.1016/j.ceh.2024.08.001 · Cited by 35

MRI Brain Tumor Detection Methods Using Contourlet Transform Based on Time Adaptive Self-Organizing Map

Farzamnia, A., Hazaveh, S. H., Siadat, S. S., & Moung, E. G. (2023) · IEEE Access · doi:10.1109/access.2023.3322450 · Cited by 34

Advancements in brain tumor analysis: a comprehensive review of machine learning, hybrid deep learning, and transfer learning approaches for MRI-based classification and segmentation

Das, S., & Goswami, R. S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20203-0 · Cited by 34

An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network

Fayaz, M., Torokeldiev, N., Turdumamatov, S., Qureshi, M. S., Qureshi, M. B., & Gwak, J. (2021) · Sensors · doi:10.3390/s21227480 · Cited by 37

Coal and gangue classification in actual environment of mines based on deep learning

Luan, H., Xu, H., Tang, W., Tian, Y., & Zhang, Q. (2023) · Measurement · doi:10.1016/j.measurement.2023.112651 · Cited by 33

Review, Limitations, and future prospects of neural network approaches for brain tumor classification

Das, S., & Goswami, R. S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17215-7 · Cited by 33

Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification

Ullah, Z., & Kim, J. (2025) · Mathematics · doi:10.3390/math13172787 · Cited by 40

GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using Explainable AI

Rahman, M. A., Masum, M. I., Hasib, K. M., Mridha, M. F., Alfarhood, S., Safran, M., & Che, D. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.050760 · Cited by 29

Clinical Decision Support Framework for Segmentation and Classification of Brain Tumor MRIs Using a U-Net and DCNN Cascaded Learning Algorithm

Samee, N. M. A., Ahmad, T., Mahmoud, N. F., Atteia, G. E., Abdallah, H. A., & Rizwan, A. (2022) · Healthcare · doi:10.3390/healthcare10122340 · Cited by 32

IoT framework for brain tumor detection based on optimized modified ResNet 18 (OMRES)

El‐Feshawy, S. A., Saad, W., Shokair, M., & Dessouky, M. I. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-022-04678-y · Cited by 28

Enhancing brain tumor classification with transfer learning: Leveraging DenseNet121 for accurate and efficient detection

Raza, A., Alshehri, M. S., Almakdi, S., Siddique, A. A., Alsulami, M., & Alhaisoni, M. M. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22957 · Cited by 27

Novel probabilistic resource migration algorithm for cross-cloud live migration of virtual machines in public cloud

Pal, S., Kumar, R., Sơn, L. H., Saravanan, K. A., Abdel‐Basset, M., Manogaran, G., & Thong, P. H. (2019) · The Journal of Supercomputing · doi:10.1007/s11227-019-02874-x · Cited by 27

Comparative Analysis of Resource-Efficient CNN Architectures for Brain Tumor Classification

Khan, M., & Auvee, R. B. Z. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11021970 · Cited by 30

A BERT Based Approach to Measure Web Services Policies Compliance With GDPR

Elluri, L., Chukkapalli, S. S. L., Joshi, K. P., Finin, T., & Joshi, A. (2021) · IEEE Access · doi:10.1109/access.2021.3123950 · Cited by 25

Multi-Classification of Brain Tumors via Feature Level Ensemble of Convolutional Neural Networks

Aurna, N. F., Yousuf, M. A., & Taher, K. A. (2021) · International Conference on Sustainable Technologies for Industry 5.0 (STI) · doi:10.1109/sti53101.2021.9732543 · Cited by 23

Context aware machine learning techniques for brain tumor classification and detection – A review

Amjad, U., Raza, A., Fahad, M., Farid, D., Akhunzada, A., Abubakar, M., & Beenish, H. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e41835 · Cited by 24

Image-based state-of-the-art techniques for the identification and classification of brain diseases: a review

Haq, E. U., Huang, J., Li, K., Haq, H. U., & Zhan, T. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02256-z · Cited by 22

Optimizing Tumor Classification Through Transfer Learning and Particle Swarm Optimization-Driven Feature Extraction

Siddique, A. A., Raza, A., Alshehri, M. S., Alasbali, N. A., & Abbasi, S. F. (2024) · IEEE Access · doi:10.1109/access.2024.3412412 · Cited by 22

Deep learning frameworks for MRI-based diagnosis of neurological disorders: a systematic review and meta-analysis

Ali, S. S. A., Memon, K., Yahya, N., & Khan, S. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11146-5 · Cited by 24

Brain Tumor Classification Using Decision Tree and Neural Network Classifiers

Subbarao, M. V., Kumar, T. S., Chowdary, P. S. R., & Chakravarthy, V. V. S. S. S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-1559-8_41 · Cited by 17

A comparative analysis and classification of cancerous brain tumors detection based on classical machine learning and deep transfer learning models

Singh, Y. P., & Lobiyal, D. K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16637-7 · Cited by 19

GraphMriNet: a few-shot brain tumor MRI image classification model based on Prewitt operator and graph isomorphic network

Liao, B., Zuo, H., Yu, Y., & Li, Y. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01530-z · Cited by 17

An explainable ensemble approach for advanced brain tumor classification applying Dual-GAN mechanism and feature extraction techniques over highly imbalanced data

Roy, P., Srijon, F. M. S., & Bhowmik, P. (2024) · PLoS ONE · doi:10.1371/journal.pone.0310748 · Cited by 16

Enhancing Brain Tumor Detection Through Custom Convolutional Neural Networks and Interpretability-Driven Analysis

Dewage, K. A. K. W., Hasan, R., Rehman, B., & Mahmood, S. (2024) · Information · doi:10.3390/info15100653 · Cited by 17

Convolutional neural-network-based classification of retinal images with different combinations of filtering techniques

Henry, A. G. P., & Anitha, J. (2021) · Open Computer Science · doi:10.1515/comp-2020-0177 · Cited by 15

Hybrid Deep Learning Models for Multi-classification of Tumour from Brain MRI

Munira, H. A., & Islam, M. S. (2022) · Journal of Information Systems Engineering and Business Intelligence · doi:10.20473/jisebi.8.2.162-174 · Cited by 14

Brain tumour classification: a comprehensive systematic review on various constraints

Chaithanyadas, K. V., & King, G. R. G. (2022) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2022.2083019 · Cited by 13

LSTM-ESA HİBRİT MODELİ İLE MR GÖRÜNTÜLERİNDEN BEYİN TÜMÖRÜNÜN SINIFLANDIRILMASI

Aslan, E. (2024) · Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.54365/adyumbd.1391157 · Cited by 13

A novel multi-class brain tumor classification method based on unsupervised PCANet features

Shahin, A. I., Aly, S., & Aly, W. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08281-x · Cited by 13

Adaptive convolutional neural network using N-gram for spatial object recognition

Bapu, J. J., Florinabel, D. J., Robinson, Y. H., Julie, E., Kumar, R., Vo, N. T. N., Son, L.-H.-A., Trần, T. M., & Giap, C. N. (2019) · Earth Science Informatics · doi:10.1007/s12145-019-00396-x · Cited by 11

Provably Efficient Multi-Cancer Image Segmentation Based on Multi-Class Fuzzy Entropy

Jasim, H. M., Ghrabat, M. J. J., Abdulrahman, L. Q., Nyangaresi, V. O., Ma, J., Abduljabbar, Z. A., & Abduljaleel, I. Q. (2023) · Informatica · doi:10.31449/inf.v47i8.4840 · Cited by 12

An Efficient Method for Diagnosing Brain Tumors Based on MRI Images Using Deep Convolutional Neural Networks

Han, T. T., Van, H. N., Thanh, H. N. T., Vu, T. A., Tuan, D. N., & Vu, D. L. (2022) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2022/2092985 · Cited by 14

Performance Analysis of Classification Techniques of Human Brain MRI Images

Chauhan, N., & Choi, B.-J. (2019) · International Journal of Fuzzy Logic and Intelligent Systems · doi:10.5391/ijfis.2019.19.4.315 · Cited by 12

A deep learning based system for accurate diagnosis of brain tumors using T1-w MRI

Ahmed, M., Khalifa, F., Moustafa, H. E.-D., Saleh, G. A., & AbdElhalim, E. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v28.i2.pp1192-1202 · Cited by 11

Performance Analysis of Brain Tumor Classification on MRI images using Pretrained Deep Learning ModelsPreprint

Manjunath, R. V., Yashaswini Gowda, N., Manu, H. M., & Lutimath, N. M. (2024) · Research Square · doi:10.21203/rs.3.rs-3998886/v1 · Cited by 11

Neutrosophic approach for enhancing quality of signals

Jha, S., Kumar, R., Sơn, L. H., Chiclana, F., Puri, V., & Priyadarshini, I. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7375-0 · Cited by 10

A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

Narayanan, K. L., Krishnan, R. S., Julie, E. G., Robinson, H., Kumar, R., Sơn, L. H., Hung, T. X., Samui, P., Ngo, P. T. T., & Bui, D. T. (2020) · Applied Sciences · doi:10.3390/app10020718 · Cited by 10

Ensemble Transfer Learning for Brain Tumor Classification

Remzan, N., Tahiry, K., & Farchi, A. (2022) · International Conference on Advanced Communication Technologies and Networking (CommNet) · doi:10.1109/commnet56067.2022.9993831 · Cited by 10

Brain Tumor Classification using Ensemble Classifiers

Subbarao, M. V., Ram, G. C., Kumar, D. G., & Terlapu, S. K. (2022) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears53579.2022.9752177 · Cited by 9

Performance enhanced ripplet transform based compression method for medical images

Anitha, J., Sophia, P. E., Sơn, L. H., & de Albuquerque, V. H. C. (2019) · Measurement · doi:10.1016/j.measurement.2019.04.036 · Cited by 9

Combined CNN and Pixel Feature Image for Fatty Liver Ultrasound Image Classification

Zhu, H., Liu, Y., Gao, X., & Zhang, L. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/9385734 · Cited by 8

Performance Analysis of Machine Learning Techniques for Multi-Organ Cancer Detection and Classification: A Comparative Study

Priya, M. L. V. A., & Subbarao, M. V. (2023) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs57948.2023.10434014 · Cited by 8

Brain tumor classification on MRI images using fine-tuned deep learning models

Manjunath, R. V., Yashaswini Gowda, N., Manu, H. M., Bharath, K. N., & Manasa, R. (2025) · Machine learning for computational science and engineering · doi:10.1007/s44379-025-00024-0 · Cited by 7

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