Brain Tumor Detection Based on Multimodal Information Fusion and Convolutional Neural Network

ImageNet classification with deep convolutional neural networks

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Backpropagation Applied to Handwritten Zip Code Recognition

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The multimodal brain tumor image segmentation benchmark (BRATS)

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Brain tumor segmentation with Deep Neural Networks

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Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images

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A deep learning model integrating FCNNs and CRFs for brain tumor segmentation

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Automatic segmentation of non-enhancing brain tumors in magnetic resonance images

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Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor

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Comparison of normalization methods for the analysis of metagenomic gene abundance data

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Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Khan, M. A., Lali, M. I. U., Rehman, A. U., Ishaq, M., Sharif, M. I., Saba, T., Zahoor, S., & Akram, T. (2019) · Microscopy Research and Technique · doi:10.1002/jemt.23238 · Cited by 186

Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features

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Machine learning based brain tumour segmentation on limited data using local texture and abnormality

Bonte, S., Goethals, I., & Holen, R. V. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.005 · Cited by 79

Detecting newly grown tree leaves from unmanned-aerial-vehicle images using hyperspectral target detection techniques

Lin, C., Chen, S.-Y., Chen, C., & Tai, C.-H. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.05.022 · Cited by 65

Prediction of Wheat Production Using Artificial Neural Networks and Investigating Indirect Factors Affecting It: Case Study in Canterbury Province, New Zealand

Safa, M., Samarasinghe, S., & Nejat, M. (2015) · Lincoln University Research Archive (Lincoln University) · Cited by 60

Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields

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Classification of pressure ulcer tissues with 3D convolutional neural network

García-Zapirain, B., Elmogy, M. M., El-Baz, A. S., & Elmaghraby, A. S. (2018) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-018-1835-y · Cited by 59

Stacked sparse autoencoder and history of binary motion image for human activity recognition

Gnouma, M., Ladjailia, A., Ejbali, R., & Zaied, M. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-6273-1 · Cited by 49

A new fuzzy clustering algorithm for the segmentation of brain tumor

Ananthi, V. P., Balasubramaniam, P., & Kalaiselvi, T. (2015) · Soft Computing · doi:10.1007/s00500-015-1775-5 · Cited by 45

Domain-Adapted Convolutional Networks for Satellite Image Classification: A Large-Scale Interactive Learning Workflow

Lunga, D., Yang, H. L., Reith, A., Weaver, J. E., Yuan, J., & Bhaduri, B. (2018) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2018.2795753 · Cited by 40

A STRATEGY OF OPTIMAL EFFICACY OF T11 TARGET STRUCTURE IN THE TREATMENT OF BRAIN TUMOR

Khajanchi, S., & Banerjee, S. (2019) · Journal of Biological Systems · doi:10.1142/s0218339019500104 · Cited by 40

Crack image detection based on fractional differential and fractal dimension

Cao, T., Wang, W., Tighe, S. L., & Wang, S. (2018) · IET Computer Vision · doi:10.1049/iet-cvi.2018.5337 · Cited by 28

Magnetic resonance imaging of the cirrhotic liver: diagnosis of hepatocellular carcinoma and evaluation of response to treatment - Part 1

Ramalho, M., Matos, A. P., AlObaidy, M., Velloni, F. G., Altun, E., & Semelka, R. C. (2017) · Radiologia Brasileira · doi:10.1590/0100-3984.2015.0132 · Cited by 27

Segmentation of Sidescan Sonar Imagery Using Markov Random Fields and Extreme Learning Machine

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A Primer on Deep Learning Architectures and Applications in Speech Processing

Ogunfunmi, T., Ramachandran, R. P., Togneri, R. B., Zhao, Y., & Xia, X. (2019) · Circuits Systems and Signal Processing · doi:10.1007/s00034-019-01157-3 · Cited by 26

Development of image-processing software for automatic segmentation of brain tumors in MR images

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Multi-Scale Segmentation and Surface Fitting for Measuring 3-D Macular Holes

Nasrulloh, A. V., Willcocks, C. G., Jackson, P. T., Geenen, C., Habib, M., STEEL, D. H., & Obara, B. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2767908 · Cited by 20

Evaluation of Network Traffic Analysis Using Fuzzy C-Means Clustering Algorithm in Mobile Malware Detection

Feizollah, A., Anuar, N. B., & Salleh, R. (2018) · Advanced Science Letters · doi:10.1166/asl.2018.10660 · Cited by 20

A Hardware Trojan Attack on FPGA-Based Cryptographic Key Generation: Impact and Detection

Govindan, V., Chakraborty, R. S., Santikellur, P., & Chaudhary, A. K. (2018) · Journal of Hardware and Systems Security · doi:10.1007/s41635-018-0042-5 · Cited by 17

Analysis and modeling of wireless channel characteristics for Internet of Things scene based on geometric features

Shi, G., He, Y., Li, B., Zuo, L., Yin, B., Zeng, W., & Ali, F. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.06.032 · Cited by 16

Diferencias en la tasa de atrofia global y regional y del volumen lesional entre género en esclerosis múltiple

Rojas, J. I., Patrucco, L., Besada, C. H., Funes, J., & Cristiano, E. (2012) · Neurología · doi:10.1016/j.nrl.2012.10.008 · Cited by 10

Comparative study between spatio-temporal models for brain tumor growth

Elaff, I. (2018) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2018.01.183 · Cited by 8

A Mixed Non‐local Prior Model for Image Super‐resolution Reconstruction

Zhao, S., Lyu, Z., Hu, L., & Sarem, M. (2017) · Chinese Journal of Electronics · doi:10.1049/cje.2016.06.024 · Cited by 7

MOSnet: moving object segmentation with convolutional networks

Jeong, J., Yoon, T. S., & Park, J. (2017) · Electronics Letters · doi:10.1049/el.2017.3982 · Cited by 7

Computerized detection of spina bifida using SVM with Zernike moments of fetal skulls in ultrasound screening

Konur, U. (2018) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2018.02.012 · Cited by 7

Information integration of force sensing and machine vision for in‐shell shrivelled walnut detection based on the golden‐section search optimal discrimination threshold

Zhai, Z., Jin, Z., & Zhang, R. (2019) · Journal of the Science of Food and Agriculture · doi:10.1002/jsfa.9618 · Cited by 7

Sensitivity Study on Quantitative Methods by Analysis of Pressure Fluctuations in a Fluidized Bed

Wang, J., Chen, X., & Zhong, W. (2018) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.8b02937 · Cited by 4

Automatic CT Brain Image Segmentation Using Two Level Multiresolution Mixture Model of EM

Jiji, G. W., & Dehmeshki, J. (2014) · Journal of The Institution of Engineers (India) Series B · doi:10.1007/s40031-014-0095-y · Cited by 2

SU-D-BRA-03: What Image Features Are Useful for Tumor Segmentation in Multi-Modal Images?

Hu, Y., Grossberg, M., & Mageras, G. S. (2015) · Medical Physics · doi:10.1118/1.4923883 · Cited by 1

Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging

Abdusalomov, A. B., Mukhiddinov, M., & Whangbo, T. K. (2023) · Cancers · doi:10.3390/cancers15164172 · Cited by 606

Image Segmentation for MR Brain Tumor Detection Using Machine Learning: A Review

Soomro, T. A., Zheng, L., Afifi, A. J., Ali, A., Soomro, S., Yin, M., & Gao, J. (2022) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2022.3185292 · Cited by 389

A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet

Shah, H. A., Saeed, F., Yun, S., Park, J., Paul, A., & Kang, J. (2022) · IEEE Access · doi:10.1109/access.2022.3184113 · Cited by 379

Role of deep learning in brain tumor detection and classification (2015 to 2020): A review

Nazir, M., Shakil, S., & Khurshid, K. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101940 · Cited by 293

A Brain Tumor Identification and Classification Using Deep Learning based on CNN-LSTM Method

Vankdothu, R., Hameed, M. A., & Fatima, H. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.107960 · Cited by 269

Fusion-Based Multimodal Brain Tumor Detection Using Convolution Neural Network

Banerjee, S., Roy, S., & Das, A. (2021) · Lecture notes in bioengineering · doi:10.1007/978-981-33-6915-3_19

Enhanced Brain Tumor Diagnosis through Multimodal Fusion and 3D Convolutional Neural Networks

J, C. M. B. M., Kannan, E., Alex David, S., Thangarasu, G., & Begum, A. (2023) · International Conference On Smart Technologies For Smart Nation (SmartTechCon) · doi:10.1109/smarttechcon57526.2023.10391443 · Cited by 3

Multi Modal Convolutional Neural Networks for Brain Tumor SegmentationPreprint

Aygün, M. M., Şahin, Y. H., & Ünal, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.06191 · Cited by 39

Brain Tumor Detection Using Integration of Computed Tomography and Magnetic Resonance Imaging via Fully Convolutional Neural Networks

da Rocha, A. M., Serafim, F. L., & Sousa, A. A. C. (2026) · Journal of Computer Sciences and Informatics. · doi:10.5455/ejcsi.20250919030734

Learning Methods of Convolutional Neural Network Combined With Image Feature Extraction in Brain Tumor Detection

Wang, W., Bu, F., Lin, Z., & Zhai, S. (2020) · IEEE Access · doi:10.1109/access.2020.3016282 · Cited by 74