BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model

CBAM: Convolutional Block Attention Module

Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_1 · Cited by 25,853

SGDR: Stochastic Gradient Descent with Warm Restarts

Loshchilov, I., & Hutter, F. (2016) · arXiv · doi:10.48550/arxiv.1608.03983 · Cited by 5,309

Hypercolumns for object segmentation and fine-grained localization

Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511

Multi-grade brain tumor classification using deep CNN with extensive data augmentation

Sajjad, M., Khan, S., Muhammad, K., Wu, W., Ullah, A., & Baik, S. W. (2018) · Journal of Computational Science · doi:10.1016/j.jocs.2018.12.003 · Cited by 895

Adaptive batch normalization for practical domain adaptation

Li, Y., Wang, N., Shi, J., Hou, X., & Liu, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.03.005 · Cited by 601

Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network

Shree, N. V., & Kumar, T. N. R. (2018) · Brain Informatics · doi:10.1007/s40708-017-0075-5 · Cited by 490

A comparison of deep networks with ReLU activation function and linear spline-type methods

Eckle, K., & Schmidt-Hieber, J. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.11.005 · Cited by 414

Residual Networks of Residual Networks: Multilevel Residual Networks

Zhang, K., Sun, M., Han, T. X., Yuan, X., Guo, L., & Liu, T. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2654543 · Cited by 413

A Deep Feature Learning Model for Pneumonia Detection Applying a Combination of mRMR Feature Selection and Machine Learning Models

Toğaçar, M., Ergen, B., Cömert, Z., & Özyurt, F. (2019) · IRBM · doi:10.1016/j.irbm.2019.10.006 · Cited by 298

BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer

Toğaçar, M., Özkurt, K. B., Ergen, B., & Cömert, Z. (2019) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2019.123592 · Cited by 276

Deep Learning–Assisted Diagnosis of Cerebral Aneurysms Using the HeadXNet Model

Park, A., Chute, C., Rajpurkar, P., Lou, J., Ball, R. L., Shpanskaya, K., Jabarkheel, R., Kim, L. H., McKenna, E., Tseng, J., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.5600 · Cited by 258

On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning

Gao, B., & Pavel, L. (2017) · arXiv · doi:10.48550/arxiv.1704.00805 · Cited by 207

Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks

Toğaçar, M., Ergen, B., & Cömert, Z. (2019) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2019.11.004 · Cited by 207

Nutritional profiling of hilsa (Tenualosa ilisha) of different size groups and sensory evaluation of their adults from different riverine systems

De, D. K., Mukherjee, S., Anand, P. S. S., Kumar, P., Suresh, V. R., & Vijayan, K. K. (2019) · Scientific Reports · doi:10.1038/s41598-019-55845-w · Cited by 196

Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images

Budak, Ü., Cömert, Z., Rashid, Z. N., Şengür, A., & Çıbuk, M. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105765 · Cited by 169

Predictive Modeling of Brain Tumor: A Deep Learning Approach

Saxena, P., Maheshwari, A., & Maheshwari, S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-6067-5_30 · Cited by 156

Brain wave classification using long short-term memory network based OPTICAL predictor

Kumar, S., Sharma, A. K., & Tsunoda, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-45605-1 · Cited by 129

Object Instance Segmentation and Fine-Grained Localization Using Hypercolumns

Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2578328 · Cited by 102

Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders

Toğaçar, M., Ergen, B., & Cömert, Z. (2019) · Medical Hypotheses · doi:10.1016/j.mehy.2019.109503 · Cited by 95

A simple and intelligent approach for brain MRI classification

Nazir, M., Wahid, F., & Khan, S. A. (2015) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/ifs-141396 · Cited by 82

Improved Faster R-CNN Traffic Sign Detection Based on a Second Region of Interest and Highly Possible Regions Proposal Network

Shao, F., Wang, X., Meng, F., Zhu, J., Wang, D., & Dai, J. (2019) · Sensors · doi:10.3390/s19102288 · Cited by 71

Prediction of intrapartum fetal hypoxia considering feature selection algorithms and machine learning models

Cömert, Z., Şengür, A., Budak, Ü., & Kocamaz, A. F. (2019) · Health Information Science and Systems · doi:10.1007/s13755-019-0079-z · Cited by 71

DCCMED-Net: Densely connected and concatenated multi Encoder-Decoder CNNs for retinal vessel extraction from fundus images

Budak, Ü., Cömert, Z., Çıbuk, M., & Şengür, A. (2019) · Medical Hypotheses · doi:10.1016/j.mehy.2019.109426 · Cited by 70

MRI Brain Images Classification: A Multi-Level Threshold Based Region Optimization Technique

Kanmani, P., & Marikkannu, P. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-0915-8 · Cited by 59

Geometry Aware Constrained Optimization Techniques for Deep Learning

Roy, S. K., Mhammedi, Z., & Harandi, M. T. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00469 · Cited by 57

Diagnosis of Eye Retinal Diseases Based on Convolutional Neural Networks Using Optical Coherence Images

Sertkaya, M. E., Ergen, B., & Toğaçar, M. (2019) · International Conference Electronics · doi:10.1109/electronics.2019.8765579 · Cited by 57

Brain tumors: Special characters for research and banking

Kheirollahi, M., Dashti, S., Khalaj, Z., Nazemroaia, F., & Mahzouni, P. (2014) · Advanced Biomedical Research · doi:10.4103/2277-9175.148261 · Cited by 54

Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

Toğaçar, M., Ergen, B., & Sertkaya, M. E. (2019) · Elektronika ir Elektrotechnika · doi:10.5755/j01.eie.25.5.24358 · Cited by 52

Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network

Başaran, E., Cömert, Z., Şengür, A., Budak, Ü., Çelik, Y., & Toğaçar, M. (2019) · International Conference on Computer Science and Engineering (UBMK) · doi:10.1109/ubmk.2019.8907070 · Cited by 47

Mini-batch algorithms with online step size

Yang, Z., Wang, C., Zhang, Z., & Li, J. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.11.031 · Cited by 41

Hybrid gray wolf optimizer–artificial neural network classification approach for magnetic resonance brain images

Ahmed, H. M., Youssef, B. A. B., Elkorany, A. S., Saleeb, A. A., & El‐Samie, F. E. A. (2017) · Applied Optics · doi:10.1364/ao.57.000b25 · Cited by 36

Cancer around the brain

Grisold, W., & Grisold, A. (2014) · Neuro-Oncology Practice · doi:10.1093/nop/npt002 · Cited by 31

DEEP LEARNING APPROACH FOR CLASSIFICATION OF BREAST CANCER

Toğaçar, M., & Ergen, B. (2018) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap.2018.8620802 · Cited by 31

Minimizing Medical Radiation Exposure by Incorporating a New Radiation “Vital Sign” into the Electronic Medical Record: Quality of Care and Patient Safety

Lukoff, J., & Olmos, J. (2017) · The Permanente Journal · doi:10.7812/tpp/17-007 · Cited by 28

Sign Language Recognition Based on CBAM-ResNet

Chao, H. G., Fenhua, W., & Zhang, R. (2019) · International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM) · doi:10.1145/3358331.3358379 · Cited by 18

Change Detection in Synthetic Aperture Radar Images based on Convolutional Block Attention Module

Wang, D., Gao, F., Dong, J., & Wang, S. (2019) · International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) · doi:10.1109/multi-temp.2019.8866962 · Cited by 14

Skin Lesion Segmentation using Deep Hypercolumn Descriptors

Ramachandram, D., & Taylor, G. W. (2017) · Journal of Computational Vision and Imaging Systems · doi:10.15353/vsnl.v3i1.173 · Cited by 11

Biyomedikal Görüntülerde Derin Öğrenme ile Mevcut Yöntemlerin Kıyaslanması

Toğaçar, M., & Ergen, B. (2019) · DergiPark (Istanbul University) · Cited by 11

Deep MLP neural network control of bioreactor

Salah, L. B., & Fourati, F. (2019) · International Renewable Energy Congress · doi:10.1109/irec.2019.8754572 · Cited by 9

ANALISIS PERFORMA ALGORITMA NAIVE BAYES PADA DETEKSI OTOMATIS CITRA MRI

Akbar, F., Rais, A. N., Sobari, I. A., Zuama, R. A., & Rudiarto, B. (2019) · JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) · doi:10.33480/jitk.v5i1.586 · Cited by 8

A Simple yet Effective Method to Prune Dense Layers of Neural Networks

Babaeizadeh, M., Smaragdis, P., & Campbell, R. H. (2017) · Cited by 7

Densely connected convolutional network block based autoencoder for panorama map compression

Wang, S., Wang, H., Xiang, S., & Yu, L. (2019) · Signal Processing Image Communication · doi:10.1016/j.image.2019.115678 · Cited by 7

Zatürre Hastalığının Derin Öğrenme Modeli ile Tespiti

Toğaçar, M., Ergen, B., & Sertkaya, M. E. (2019) · DSpace - FIRAT (Fırat University) · Cited by 6

Improving Residual Block for Semantic Image Segmentation

Liu, F., Liu, J., Fu, J., & Lu, H. (2018) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2018.8499452 · Cited by 5

Hypercolumn Sparsification for Low-Power Convolutional Neural Networks

Pilly, P. K., Stepp, N. D., Liapis, Y., Payton, D. W., & Srinivasa, N. (2019) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/3304104 · Cited by 5

Deep Anchored Convolutional Neural Networks

Huang, J., Dwivedi, K., & Roig, G. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00089 · Cited by 4

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Toğaçar, M., Ergen, B., & Cömert, Z. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103805 · Cited by 574

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

Nayak, S. R., Nayak, D. R., Sinha, U., Arora, V., & Pachori, R. B. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102365 · Cited by 401

A Novel Medical Diagnosis model for COVID-19 infection detection based on Deep Features and Bayesian Optimization

Nour, M., Cömert, Z., & Polat, K. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106580 · Cited by 316

Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM

Maqsood, S., Damaševičius, R., & Maskeliūnas, R. (2022) · Medicina · doi:10.3390/medicina58081090 · Cited by 313

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

Khan, P., Kader, M. F., Islam, S. M. R., Rahman, A. B., Kamal, M. S., Toha, M. U., & Kwak, K. S. (2021) · IEEE Access · doi:10.1109/access.2021.3062484 · Cited by 314

Classification of brain MRI using hyper column technique with convolutional neural network and feature selection method

Toğaçar, M., Cömert, Z., & Ergen, B. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113274 · Cited by 132

Tumor type detection in brain MR images of the deep model developed using hypercolumn technique, attention modules, and residual blocks

Toğaçar, M., Ergen, B., & Cömert, Z. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02290-x · Cited by 69

A novel convolutional neural network‐based approach for brain tumor classification using magnetic resonance images

Çınar, N., Kaya, M., & Kaya, B. (2022) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22839 · Cited by 37

Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture

Çınar, A., & Yıldırım, M. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2020.109684 · Cited by 434

Detection of Brain Tumor Using Novel Convolutional Neural Network with Magnetic Resonance Imaging

Devi, K., & Sharma, A. (2023) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip61524.2023.10537668 · Cited by 1