Convolutional neural networks for multi-class brain disease detection using MRI images

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

Untitled work

(2014) · Cited by 33,419

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818

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

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint

Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273

Neurophysiological investigation of the basis of the fMRI signal

Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551

How transferable are features in deep neural networks?

Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061

Automatic COVID-19 pneumonia diagnosis from x-ray lung image: A Deep Feature and Machine Learning Solution

Ahmed, M. A., Al-Qaysi, Z. T., Shuwandy, M. L., Salih, M. M., & Ali, M. H. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1963/1/012099 · Cited by 25

Multi-resolution visual Mamba with multi-directional selective mechanism for retinal disease detection

Zuo, Q., Shi, Z., Bo, L., Ping, N., Wang, J., Cheng, X., Zhang, K., Guo, J., Wu, Y., & Hong, J. (2024) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2024.1484880 · Cited by 23

Detection of brain tumor with a pre-trained deep learning model based on feature selection using MR images

Demir, K., Arı, B. G., & Demir, F. (2023) · Firat University Journal of Experimental and Computational Engineering · doi:10.5505/fujece.2023.36844 · Cited by 21

IFAS: improved fully automatic segmentation convolutional neural network model along with morphological segmentation for brain tumor detection

Kulshreshtha, A., & Nagpal, A. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01572-5 · Cited by 21

EfficientCovNet: A CNN-based approach to detect various pulmonary diseases including COVID-19 using modified EfficientNet

Argho, A. G., Maswood, M. M. S., Mahmood, M. I., & Mondol, N. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200315 · Cited by 22

Image Retrieval Based on Deep Learning

(2022) · Journal of System and Management Sciences · doi:10.33168/jsms.2022.0226 · Cited by 21

Brain MRI segmentation techniques based on CNN and its variants

Tripathy, B. K., Parikh, S., Ajay, P., & Magapu, C. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-91171-9.00001-6 · Cited by 20

A Practical Multiclass Classification Network for the Diagnosis of Alzheimer’s Disease

Khan, R., Qaisar, Z. H., Mehmood, A., Ali, G., Alkhalifah, T., Alturise, F., & Wang, L. (2022) · Applied Sciences · doi:10.3390/app12136507 · Cited by 20

Feature-level ensemble approach for COVID-19 detection using chest X-ray images

Ho, T. K. K., & Gwak, J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268430 · Cited by 20

JMCD Dataset for Brain Tumor Detection and Analysis Using Explainable Deep Learning

Verma, A., Gupta, N., Bhatele, P., & Khanna, P. (2023) · SN Computer Science · doi:10.1007/s42979-023-02308-9 · Cited by 20

CAD-ALZ: A Blockwise Fine-Tuning Strategy on Convolutional Model and Random Forest Classifier for Recognition of Multistage Alzheimer’s Disease

Abbas, Q., Hussain, A., & Baig, A. R. (2023) · Diagnostics · doi:10.3390/diagnostics13010167 · Cited by 18

An Explainable Artificial Intelligence Integrated System for Automatic Detection of Dengue From Images of Blood Smears Using Transfer Learning

Mayrose, H., Sampathila, N., Bairy, G. M., Nayak, T., Belurkar, S., & Saravu, K. (2024) · IEEE Access · doi:10.1109/access.2024.3378516 · Cited by 18

CCBlock: an effective use of deep learning for automatic diagnosis of COVID-19 using X-ray images

Al-Bawi, A., Al-Kaabi, K., Jeryo, M., & Al-Fatlawi, A. (2020) · Research on Biomedical Engineering · doi:10.1007/s42600-020-00110-7 · Cited by 18

Differentiation of Glioma Mimicking Encephalitis and Encephalitis Using Multiparametric MR-Based Deep Learning

Wu, W., Li, J., Ye, J., Wang, Q., Zhang, W., & Xu, S. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.639062 · Cited by 17

A Deep Learning Framework Integrating the Spectral and Spatial Features for Image-Assisted Medical Diagnostics

Ghosh, S., Das, S., & Mallipeddi, R. (2021) · IEEE Access · doi:10.1109/access.2021.3133338 · Cited by 17

From Sensors to Sustainability: Integrating Welfare, Management, and Climate Resilience in Small Ruminant Farm Systems

Ciliberti, M. G., Albenzio, M., & Sevi, A. (2025) · Animals · doi:10.3390/ani15223240 · Cited by 16

Convolutional Neural Network-Based Approach to Detect COVID-19 from Chest X-Ray Images

Pandiaraja, P., & Muthumanickam, K. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-8664-1_21 · Cited by 16

Combining Local and Global Feature Extraction for Brain Tumor Classification: A Vision Transformer and iResNet Hybrid Model

Jaffar, A. Y. (2024) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.8271 · Cited by 16

Detecting neonatal acute bilirubin encephalopathy based on T1-weighted MRI images and learning-based approaches

Wu, M., Shen, X., Lai, C., Zheng, W., Li, Y., Shangguan, Z., Yan, C., Liu, T., & Wu, D. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00634-z · Cited by 16

COVID-19 Along with Other Chest Infection Diagnoses Using Faster R-CNN and Generative Adversarial Network

Mostafiz, R., Uddin, M. S., Uddin, K. M. M., & Rahman, M. M. (2022) · ACM Transactions on Spatial Algorithms and Systems · doi:10.1145/3520125 · Cited by 15

The expert's knowledge combined with AI outperforms AI alone in seizure onset zone localization using resting state fMRI

Kamboj, P., Banerjee, A., Boerwinkle, V. L., & Gupta, S. K. S. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2023.1324461 · Cited by 15

Diagnosis of pes planus from X-ray images: Enhanced feature selection with deep learning and machine learning techniques

Danacı, Ç., Avcı, D., & Tuncer, S. A. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107769 · Cited by 15

Patch-based CNN for corneal segmentation of AS-OCT images: Effect of the number of classes and image quality upon performance

Marin, Y. G., Alonso‐Caneiro, D., Fisher, D., Vincent, S. J., & Collins, M. J. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106342 · Cited by 14

Motion Artifact Reduction Using U-Net Model with Three-Dimensional Simulation-Based Datasets for Brain Magnetic Resonance Images

Kang, S.-H., & Lee, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030227 · Cited by 14

Comparison of two convolutional neural network models for automated classification of brain cancer types

Fuad, M. S., Anam, C., Adi, K., & Dougherty, G. (2021) · AIP conference proceedings · doi:10.1063/5.0047750 · Cited by 14

Brain Cancer Survival Prediction on Treatment-Naïve MRI using Deep Anchor Attention Learning with Vision Transformer

Xu, X., & Prasanna, P. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761515 · Cited by 17

Alzheimer’s Disease Multi-class Classification Model Based on CNN and StackNet Using Brain MRI Data

Techa, C., Ridouani, M., Hassouni, L., & Anoun, H. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-20601-6_23 · Cited by 14

Retinal disease classification based on optical coherence tomography images using convolutional neural networks

Stanojević, M., Drašković, D., & Nikolić, B. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.32.3.032004 · Cited by 14

Experiment protocols for brain-body imaging of locomotion: A systematic review

Korivand, S., Jalili, N., & Gong, J. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1051500 · Cited by 14

COVID-19 X-rays Model Detection Using Convolution Neural Network

Ghaleb, M. S., Ebied, H. M., Shedeed, H. A., & Tolba, M. F. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-76346-6_1 · Cited by 14

An estimation method of maize impurity rate based on the deep residual networks

Yu, H., Li, Z.-Z., Guo, W., Li, D., Wang, L.-J., & Wang, Y. (2023) · Industrial Crops and Products · doi:10.1016/j.indcrop.2023.116455 · Cited by 13

Classification of COVID-19 on Chest X-Ray Images Using Deep Learning Model with Histogram Equalization and Lung Segmentation

Verma, K., Sikka, G., Swaraj, A., Kumar, S., & Kumar, A., V (2024) · SN Computer Science · doi:10.1007/s42979-024-02695-7 · Cited by 16

Differentiation Between Glioblastoma and Metastatic Disease on Conventional MRI Imaging Using 3D-Convolutional Neural Networks: Model Development and Validation

Bathla, G., Durjoy, D., Liu, Y., Le, N. H., Soni, N., Zhang, H., Mohan, S., Roberts-Wolfe, D., Rathore, S., Milan Sonka, A., et al. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.10.044 · Cited by 14

A novel privacy-supporting 2-class classification technique for brain MRI images

Devi, S., Sahoo, M. N., & Bakshi, S. (2020) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2020.05.005 · Cited by 12

Application of novel DIRF feature selection algorithm for automated brain disease detection

Yaman, S., Ünlü, E. I., Güler, H., Şengür, A., & Acharya, U. R. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105006 · Cited by 12

Automated Detection of Covid-19 Coronavirus Cases Using Deep Neural Networks with X-ray Images

Aboughazala, L. M. (2020) · Al-Azhar University Journal of Virus Researches and Studies · doi:10.21608/aujv.2020.106722 · Cited by 12

Impact Of Machine Learning Models In Pneumonia Diagnosis With Features Extracted From Chest X-Rays Using VGG16

Chowdary, G. J. (2021) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v12i5.2119 · Cited by 13

Deep Learning Approach for Analyzing the COVID-19 Chest X-Rays

Manav, M., Goyal, M., Kumar, A., Arya, A., Singh, H. D., & Yadav, A. K. (2021) · Journal of Medical Physics · doi:10.4103/jmp.jmp_22_21 · Cited by 12

New Deep Learning Models for Medical Imaging: Deep Belief Network, GAN, Autoencoder

Gupta, R., Shukla, S. K., & Tripathi, V. (2023) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec58147.2023.10276257 · Cited by 11

PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI

Kaplan, E., Chan, W. Y., Altınsoy, H. B., Bayğın, M., Barua, P. D., Chakraborty, S., Doğan, Ş., Tuncer, T., & Acharya, U. R. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00889-8 · Cited by 11

Classification Of X-ray COVID-19 Image Using Convolutional Neural Network

James, R. M., Kusrini, K., & Arief, M. R. (2020) · International Conference on Cybernetics and Intelligent System (ICORIS) · doi:10.1109/icoris50180.2020.9320828 · Cited by 11

Stage-independent biomarkers for Alzheimer’s disease from the living retina: an animal study

Ferreira, H. R. L., Serranho, P., Guimarães, P., Trindade, R., Martins, J., Moreira, P. I., Ambrósio, A. F., Castelo-Branco, M. S., & Bernardes, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-18113-y · Cited by 12

Review on Mechanobiological Analysis and Computational Study of Human Tissue (Soft and Hard) Using Machine Learning Techniques: A Mechanical Perspective

Meher, A. K., Kumar, E. K., Gangwar, A., Panda, S. K., & Pradhan, R. C. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-10003-4 · Cited by 10

Yeni Corona virüs hastalığının önceden eğitilmiş evrişimli sinir ağları kullanılarak göğüs röntgen görüntülerinden tespiti

Narin, A., & İşler, Y. (2021) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gazimmfd.827921 · Cited by 11

Efficient and accurate diagnosis of otomycosis using an ensemble deep-learning model

Mao, C., Li, A., Hu, J., Wang, P., Peng, D., Wang, J., & Sun, Y. (2022) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2022.951432 · Cited by 10

Deep-Stacked Convolutional Neural Networks for Brain Abnormality Classification Based on MRI Images

Rumala, D. J., van Ooijen, P. M. A., Rachmadi, R. F., Sensusiati, A. D., & Purnama, I. K. E. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00828-7 · Cited by 10

A comparative study on deep feature selection methods for skin lesion classification

Golnoori, F., Boroujeni, F. Z., & Monadjemi, S. A. (2023) · IET Image Processing · doi:10.1049/ipr2.13002 · Cited by 11

A deep learning model based on MRI for prediction of vessels encapsulating tumour clusters and prognosis in hepatocellular carcinoma

Yang, J., Dong, X., Wang, F., Jin, S., Zhang, B., Zhang, H., Pan, W., Gan, M., Duan, S., Zhang, L., et al. (2024) · Abdominal Radiology · doi:10.1007/s00261-023-04141-3 · Cited by 10

Automated Deep Learning of COVID-19 and Pneumonia Detection Using Google AutoML

Hussain, S. I., & Ruza, N. (2021) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.020508 · Cited by 10

Who is behind the Mask: Periocular Biometrics when Face Recognition Fails

Kumar, G., Zaveri, M. A., Bakshi, S., & Sa, P. K. (2022) · International Conference on Power, Control and Computing Technologies (ICPC2T) · doi:10.1109/icpc2t53885.2022.9777027 · Cited by 10

Feature fusion based VGGFusionNet model to detect COVID-19 patients utilizing computed tomography scan images

Uddin, K. M. M., Dey, S. K., Babu, H. M. H., Mostafiz, R., Uddin, S., Shoombuatong, W., & Moni, M. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-25539-x · Cited by 10

Enhanced neurological anomaly detection in MRI images using deep convolutional neural networks

Buttar, A. M., Shaheen, Z., Gumaei, A., Mosleh, M. A. A., Gupta, I., Alzanin, S. M., & Akbar, M. A. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1504545 · Cited by 9

Transformative Advances in AI for Precise Cancer Detection: A Comprehensive Review of Non-Invasive Techniques

Rai, H. M., Yoo, J., & Dashkevych, S. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10219-y · Cited by 9

Deep learning to identify stroke within 4.5 h using DWI and FLAIR in a prospective multicenter study

Namgung, E., Kim, Y. S., Lee, E., Chang, D., Cho, H., Lee, J., Cha, J., Park, M., Yu, K., Jung, J., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-10804-6 · Cited by 9

Brain Stroke Disease Detection: A Review

Bashir, S., Firdous, F., Rufai, S. Z., & Kumar, S. (2023) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss58487.2023.10250666 · Cited by 9

Detection of multi‐class lung diseases based on customized neural network

Ali, A., Wang, Y., & Shi, X. (2024) · Computational Intelligence · doi:10.1111/coin.12649 · Cited by 9

Automated Classification of Collateral Circulation for Ischemic Stroke in Cone-Beam CT Images Using VGG11: A Deep Learning Approach

Ali, N. H., Abdullah, A. R., Saad, N. M., Muda, A. S., & Noor, E. E. M. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4030091 · Cited by 9

Class dependency-based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of tuberculosis from chest X-rays

Chowdary, G. J., Suganya, G., Premalatha, M., & Karunamurthy, K. (2022) · doi:10.1049/pbhe040e_ch3 · Cited by 9

COMPARATIVE ANALYSIS OF PERFORMANCE OF DEEP CNN BASED FRAMEWORK FOR BRAIN MRI CLASSIFICATION USING TRANSFER LEARNING

Kulkarni, S. M. (2021) · Cited by 9

Transfer Learning from Non-Medical Images to Medical Images Using Deep Learning Algorithms

Osmani, N., Rezayi, S., Esmaeeli, E., & Karimi, A. (2024) · Frontiers in Health Informatics · doi:10.30699/fhi.v13i0.549 · Cited by 9

A Systematic Literature Review of XAI-based Approaches on Brain Disease Detection using Brain MRI Images

Madapatha, S., & Fernando, P. (2024) · International Conference on Advanced Research in Computing (ICARC) · doi:10.1109/icarc61713.2024.10499752 · Cited by 8

Lightweight convolutional neural networks using nonlinear Lévy chaotic moth flame optimisation for brain tumour classification via efficient hyperparameter tuning

Dehkordi, A. A., Neshat, M., Khosravian, A., Thilakaratne, M., Sadiq, A. S., & Mirjalili, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-02890-3 · Cited by 8

How accurate is AI in detecting marginal jaw bone loss? A systematic review and meta-analysis

Chiang, H., Jonzén, K., Wu, W., Öhberg, F., Garoff, M., Lövgren, A., & Lundberg, P. (2025) · Journal of Dentistry · doi:10.1016/j.jdent.2025.106151 · Cited by 9

CovidSORT: Detection of Novel COVID-19 in Chest X-ray Images by Leveraging Deep Transfer Learning Models

Tammina, S. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-3690-5_37 · Cited by 8

Content-Based Image Retrieval Using Fused Convolutional Neural Networks

Ghaleb, M. S., Ebied, H. M., Shedeed, H. A., & Tolba, M. F. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-20601-6_24 · Cited by 9

Neural Network for Image Classification of Laryngeal Cancer

Wu, F., Wu, P., Hou, Y., & Shang, H. (2021) · International Conference on Networking Systems of AI (INSAI) · doi:10.1109/insai54028.2021.00051 · Cited by 8

A Survey on Convolutional Neural Networks for MRI Analysis

Hardaha, S., Edla, D. R., & Parne, S. R. (2022) · Wireless Personal Communications · doi:10.1007/s11277-022-09989-0 · Cited by 8

Study of radiochemotherapy decision-making for young high-risk low-grade glioma patients using a macroscopic and microscopic combined radiomics model

Wu, G., Shi, Z., Li, Z., Xie, X., Tang, Q., Zhu, J., Yang, Z. H., Wang, Y., Wu, J., & Yu, J. (2023) · European Radiology · doi:10.1007/s00330-023-10378-9 · Cited by 8

Discovering artistic influences of painters from expressionism, impressionism, and surrealism art movements using convolutional neural network

Narag, M. J. G., & Soriano, M. (2021) · Journal of Cultural Heritage · doi:10.1016/j.culher.2021.08.015 · Cited by 7

Music Genre Recommendations Based on Spectrogram Analysis Using Convolutional Neural Network Algorithm with RESNET-50 and VGG-16 Architecture

Purnama, N. (2022) · JISA(Jurnal Informatika dan Sains) · doi:10.31326/jisa.v5i1.1270 · Cited by 7

Multi-classification of Brain Tumor Images Using Transfer Learning Based Deep Neural Network

Dutta, P., Sathi, K. A., & Islam, M. S. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-2183-3_87 · Cited by 7

Computerised Classification of MRI Images using Machine Learning Algorithms

Basha, C. Z., Likhitha, A., Alekhya, P., & Aparna, V. (2020) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc48915.2020.9155789 · Cited by 7

Brain Tumor Detection Using CNN

Beatrice, S., Thanki, J. Y., Dharia, T. A., & DCosta, C. J. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.47109 · Cited by 7

Unlocking the Potential of Medical Imaging with ChatGPT's Intelligent Diagnostics

Alzahem, A., Latif, S., Boulila, W., & Koubâa, A. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.10.496 · Cited by 7

MRI brain tumor classification using RFENet Three-Branch model with SwishReLU

Tang, Z., Liao, X., Liao, B., Shen, C., & Zhang, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108893 · Cited by 6

SMDFnet: Saliency multiscale dense fusion network for MRI and CT image fusion

Fu, J., Ouyang, A., Yang, J., Yang, D., Ge, G., Jin, H., & He, B. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109577 · Cited by 6

Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning

Pattnaik, G., Shrivastava, V. K., & Parvathi, K. (2021) · Intelligent Decision Technologies · doi:10.3233/idt-200192 · Cited by 7

Foetal neurodegenerative disease classification using improved deep ResNet classification based VGG-19 feature extraction network

Siddan, G., & Pradeepa, P. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11543-2 · Cited by 6

Deep Neural Network Classification Models for COVID-19 Detection in X-ray Images

Abdukarimov, Y., Abu, A., Altynbekov, M., Shomanov, A., Lee, S.-J., & Lee, M.-H. (2021) · International Conference on Electronics, Computer and Computation · doi:10.1109/icecco53203.2021.9663823 · Cited by 6

Lung Cancer Classification Model Using Convolution Neural Network

Hamed, E. A.-R., Salem, M. A. ‐., Badr, N. L., & Tolba, M. F. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-27762-7_2 · Cited by 6

Unraveling effects of ocular features on the performance of periocular biometrics

Kumar, G., Bakshi, S., Khan, M. A., & Albarakati, H. M. (2024) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2024.103772 · Cited by 6

Deep learning models for rapid discrimination of high-grade gliomas from solitary brain metastases using multi-plane T1-weighted contrast-enhanced (T1CE) images

Xiong, Z., Qiu, J., Liang, Q., Jiang, J., Zhao, K., Chang, H., Lv, C., Zhang, W., Li, B., Ye, J., et al. (2024) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-24-380 · Cited by 6

A lightweight network with bidirectional constraints for single image super-resolution

Chen, L., Guo, L., Cheng, D., Kou, Q., & Gao, R. (2021) · Optik · doi:10.1016/j.ijleo.2021.166818 · Cited by 6

Detection of COVID-19 Cases from Chest X-Rays using Deep Learning Feature Extractor and Multilevel Voting Classifier

Suganya, G., Premalatha, M., Geetha, S., Chowdary, G. J., & Kadry, S. (2022) · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488522500222 · Cited by 6

Artificial intelligence in diagnosis of neural disorders using biosignals and imaging

Subaşı, A., Subasi, T. N., & Özaltın, Ö. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-19073-5.00014-8 · Cited by 6

Deep Transfer Learning-Based Framework for COVID-19 Diagnosis Using Chest CT Scans and Clinical Information

Mishra, S. (2021) · SN Computer Science · doi:10.1007/s42979-021-00785-4 · Cited by 5

Bilinear MobileNets for Multi-class Brain Disease Classification Based on Magnetic Resonance Images

Rumala, D. J., Yuniarno, E. M., Rachmadi, R. F., Nugroho, S. M. S., Adrianto, Y., Sensusiati, A. D., & Purnama, I. K. E. (2021) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp52854.2021.9550987 · Cited by 5

Transfer Learning and Data Augmentation in the Diagnosis of Knee MRI

Haddadian, J., & Balamurali, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-97546-3_37 · Cited by 5

Identification And Classification Of Brain Tumor From MRI Using Transfer Learning Approach

Devi, O. R., Bindu, C. S., & Kumar, E. S. (2022) · International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) · doi:10.1109/icccis56430.2022.10037223 · Cited by 5

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