Deep convolutional neural network based medical image classification for disease diagnosis

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

ImageNet Large Scale Visual Recognition Challenge

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380

Lecture Notes in Computer Science 1205

(1999) · Industrial Robot the international journal of robotics research and application · doi:10.1108/ir.1999.04926fae.001 · Cited by 39,228

ORB: An efficient alternative to SIFT or SURF

Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126544 · Cited by 11,037

Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning

Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842

ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616

Dynamic Routing Between Capsules

Sabour, S., Frosst, N., & Hinton, G. E. (2017) · arXiv · doi:10.48550/arxiv.1710.09829 · Cited by 4,167

Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503

GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification

Frid-Adar, M., Diamant, I., Klang, E., Amitai, M. M., Goldberger, J., & Greenspan, H. K. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.013 · Cited by 2,055

Enhancing Sola Forecasting Accuracy: A Fusion of Sky Images and Meteorological Data with Channel-Wise Attention

Jonathan, A. L., Cai, D., Bamisile, O., Nkou, J. J. N., Agobah, H., & Huang, Q. (2024) · IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia) · doi:10.1109/icpsasia61913.2024.10761166 · Cited by 3

Levelwise Degradation Classification of Ancient Stone Inscriptions Images using MobileNetV2: A Deep Learning Approach

J, B. N. B., Mwatihanye, V. P., & Akhil, S. (2024) · International Conference on Sustainable Expert Systems (ICSES) · doi:10.1109/icses63445.2024.10763369 · Cited by 3

Optimization of Deep Neural Networks Using a Micro Genetic Algorithm

Becerra, R. L., Tovias-Alanis, D., & Toscano‐Pulido, G. (2024) · AI · doi:10.3390/ai5040127 · Cited by 6

Effect of Camera Choice on Image-Classification Inference

Brown, J., Nguyễn, A., & Raj, N. (2024) · Applied Sciences · doi:10.3390/app15010246 · Cited by 4

Automatic Classification and Recognition of Qinghai Embroidery Images Based on the SE‐ResNet152V2 Model

Zhao, Y., Fan, Z., Yao, H., Zhang, T., & Seng, B. (2025) · IET Image Processing · doi:10.1049/ipr2.70108 · Cited by 3

A Robust Pneumonia Classification Approach based on Self-Paced Learning

Asare, S. K., You, F., & Nartey, O. T. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110412 · Cited by 3

Extending the Classifier Algorithms in Machine Learning to Improve the Performance in Spoken Language Understanding Systems Under Deficient Training Data

Jagdale, S., & Shah, M. (2020) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj050655 · Cited by 3

Artificial Intelligence Approaches for the COVID-19 Pandemic

Srinivas, P., Chakkaravarthy, D. M., & Bhattacharyya, D. (2021) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-72752-9_13 · Cited by 2

An Assessment on Classification in Python Using Data Science

Margaret Mary, T., N, S. K., Ramanathan, G., & Clinton, G. (2021) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca51532.2021.9544704 · Cited by 3

Preliminary Classification of Selected Farmland Habitats in Ireland Using Deep Neural Networks

Abraham, L., Davy, S., Zawish, M., Mhapsekar, R. U., Finn, J. A., & Moran, P. (2022) · Sensors · doi:10.3390/s22062190 · Cited by 4

3D Reconstruction From Monocular Images Based on Deep Convolutional Networks

Ren, Y., Wang, Z., & Fan, D. (2020) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei51763.2020.9263626 · Cited by 3

Performance Comparison for Different Neural Network Architectures for chest X-Ray Image Classification

Rajadanuraks, P., Suranuntchai, S., Pechprasarn, S., & Treebupachatsakul, T. (2021) · International Conference on Engineering, Applied Sciences, and Technology (ICEAST) · doi:10.1109/iceast52143.2021.9426289 · Cited by 3

A Comparison of Machine Learning Techniques for Diagnosing Multiple Myeloma

Marimuthu, L., Pillay, N., Punchoo, R., & Bhoora, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87897-9_43 · Cited by 3

A cognitive framework based on deep neural network for classification of coronavirus disease

Kumari, S., & Bhatia, M. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-03756-6 · Cited by 3

Detection of Pancreatic Tumor from Computer Tomography Images Using 3D Convolutional Neural Network

Chaithanyadas, K. V., & King, G. R. G. (2023) · Advances in intelligent systems and computing · doi:10.1007/978-981-19-9819-5_22 · Cited by 3

An Enhanced Approach to Liver Disease Classification: Implementing Convolutional Neural Network with Attention Layer Gated Recurrent Unit

Gandikota, H. P., Abirami, S., & Kumar, M. S. (2023) · Ingénierie des systèmes d information · doi:10.18280/isi.280423 · Cited by 3

A Preliminary Research on Automatic Identification of Melanocytic Skin Lesions from Digital Images

Paja, W., Szkoła, J., Pancerz, K., Sarzyński, J., & Żychowska, M. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.10.469 · Cited by 2

Localization of Epileptogenicity Using Incomplete MRI Sequence Data in Children with Seizure Onset Zone Involving Temporal Lobe

Banerjee, S., He, Q., Lee, M.-H., Dong, M., Asano, E., & Jeong, J. (2024) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi56570.2024.10635507 · Cited by 2

DySARNet: a lightweight self-attention deep learning model for diagnosing dysarthria from speech recordings

Montalbo, F. J. P. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20053-w · Cited by 2

Opportunities and Challenges of Applying Artificial Intelligence in Healthcare

Fahmy, A. M. (2024) · Premier Journal of Artificial Intelligence · doi:10.70389/pjai.100003 · Cited by 3

Enhancing Oral Disease Classification using Dilated Convolutional Neural Network

Chakraborty, P., Saha, N., Nath, S., & Das, K. (2025) · AI-Driven Smart Healthcare for Society 5.0 · doi:10.1109/ieeeconf64992.2025.10963041 · Cited by 2

Deep Federated Learning for Real-Time Decision Making in Autonomous Vehicles

Dhage, V. N., & Ponnusamy, S. (2025) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3373-0563-9.ch014 · Cited by 2

HALO: Robust Out-of-Distribution Detection via Joint Optimisation

Keenan, H. L., Erfani, S., & Leckie, C. A. (2025) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml64287.2025.00029 · Cited by 2

Advances in Diagnostic Approaches for Alzheimer’s Disease: From Biomarkers to Deep Learning Technology

Asif, M., Ullah, H., Jamil, N., Riaz, M., Zain, M., Pushparaj, P. N., & Rasool, M. (2025) · CNS & Neurological Disorders - Drug Targets · doi:10.2174/0118715273374284250519053646 · Cited by 2

Stroke prediction algorithm based on 3D convolutional neural network for CT scans in patients with atrial fibrillation

Hsu, W.-Y., Avian, C., Leu, J., & Tsai, C. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108338 · Cited by 3

Imaging-aided diagnosis and treatment based on artificial intelligence for pulmonary nodules: A review

Gao, H., Li, J., Wu, Y., Tang, Z., He, X., Zhao, F., Chen, Y., & He, X. (2025) · Physica Medica · doi:10.1016/j.ejmp.2025.105050 · Cited by 2

Structure prediction and engineering of de novo proteins

Qi, L., & Isalan, M. (2025) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2025.141100 · Cited by 2

Enhanced Diagnosis of Chest X-Ray Using Hybrid Deep Learning Models and Feature Selection Techniques

Aziz, A., Khan, A. A., Nam, Y., & Cho, Y. (2025) · Iranian Journal of Radiology · doi:10.5812/iranjradiol-163605 · Cited by 2

From Initialization to Convergence: A Three-Stage Technique for Robust RBF Network Training

Tsoulos, I. G., Charilogis, V., & Tsalikakis, D. (2025) · AI · doi:10.3390/ai6110280 · Cited by 2

HPCSMN: A Classification Method of Chemotherapy Sensitivity of Hypopharyngeal Cancer Based on Multimodal Network

Fu, W., Li, H., Quan, X., Wang, X., Huang, W., & Zhang, H. (2025) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-025-00783-7 · Cited by 2

Usability and accuracy of an artificial intelligence-based automatic meal tray photography device for estimating liquid food intake in healthcare settings

Liu, R., Tagi, M., Suzuki, Y., Xiao, S., Hamada, Y., Ozaki, K., Kubota, M., Amano, S., Konishi, T., & Hirose, J. (2025) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2025.11.131 · Cited by 2

LCTKAN: Lightweight convolution Transformer–KAN for medical image classification

Liu, T., Yu, Y., Yang, M., Xiao, Z., Li, Q., & Tang, Y.-D. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109553 · Cited by 3

A novel multimodal deep learning architecture integrating a multi-scale gated residual block and soft attention for skin lesion classification

Ibrahimi, M. A., Abbad, K., & Abbou, R. B. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21156-2 · Cited by 2

A review of intelligent medical imaging diagnosis for the COVID-19 infection

Saurabh, N., & Shetty, J. (2022) · Intelligent Decision Technologies · doi:10.3233/idt-200178 · Cited by 2

Unsupervised Region-Based Denoising for Optical Coherence Tomography Framework

Ahmed, H., Zhang, Q., Donnan, R. S., & Alomainy, A. (2022) · International Conference on Computational Intelligence and Applications (ICCIA) · doi:10.1109/iccia55271.2022.9828454 · Cited by 2

Dynamic Channel Selection in Self-Supervised Learning

Krishna, T., Rai, A., Djilali, Y. A. D., Smeaton, A. F., McGuinness, K., & O'Connor, N. E. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/lkli8696 · Cited by 2

Classification of Chest X-ray Images Based on Superpixels and Deep Learning Models

Lu, S., Qin, Y., Che, Y., & Guo, H. (2022) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei56279.2022.9979867 · Cited by 2

Attention Based Speckle Reduction for Optical Coherence Tomography in Ophthalmology and Dentistry

Ahmed, H., Zhang, Q., Donnan, R. S., & Alomainy, A. (2022) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei56279.2022.9979980 · Cited by 2

Convolutional Neural Networks for C. Elegans Muscle Age Classification Using Only Self-learned Features

Czaplewski, B., Dzwonkowski, M., & Panas, D. (2022) · Journal of Telecommunications and Information Technology · doi:10.26636/jtit.2022.165322 · Cited by 2

A Study on Class Activation Map Methods to Detect Masses in Mammography Images using Weakly Supervised Learning

Sampaio, V., & Cordeiro, F. R. (2022) · doi:10.5753/eniac.2022.227318 · Cited by 2

Design and Implementation of the CNN Accelator Based on Multi-streaming SIMD Mechanisms

Chiu, J.-C., Yan, Z.-Y., & Liu, Y.-C. (2022) · Communications in computer and information science · doi:10.1007/978-981-19-9582-8_40 · Cited by 2

A Survey of Spectrum Sensing Algorithms Based on Machine Learning

Liu, Y., & Li, J. (2023) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-20738-9_97 · Cited by 3

Transfer Learning-based Ensemble Approach for Organ Classification: An Empirical Study

Islam, S. N., Tabassum, F., Rizwan, S., & Chowdhury, T. M. (2022) · International Conference on Electrical and Computer Engineering (ICECE) · doi:10.1109/icece57408.2022.10089089 · Cited by 2

3D Brain Tumor Segmentation on U-Net Classifier

Suja, G. P., Kaleeswari, P., Raajan, P., Prasad, A. V. S. R., Aarthi., K., & Gopatoti, A. (2023) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds56580.2023.10104868 · Cited by 2

An Approach to Automated Diagnosis and Texture Analysis of the Fetal Liver & Placenta in Fetal Growth Restriction

Zeidan, A. M., Gilliland, P. R., Patel, A., Ou, Z., Flouri, D., Mufti, N., Maksym, K., Aughwane, R., Sebastien Ourselin, A., David, A. L., et al. (2023) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2022-ac28 · Cited by 2

Implementation of Reinforcement Learning and Imaging for Better Decision-making in the Medical Sector

Rawat, R. S., & Rana, D. S. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126243 · Cited by 2

Sparsely Supervised Learning for Medical Image Classification on Noisy Heterogeneous Data

Thillai Manjari, T., Chakravarthi, S. S., Sountharrajan, S., Rao, B. N. K., Suganya, E., & Nivaashini, M. (2023) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite57410.2023.10183290 · Cited by 2

Lung Nodule Detection and Classification: Methods and Challenges

Prabhavalkar, V. S., & Arthi, K. (2023) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss58487.2023.10250621 · Cited by 2

Biggest Margin Tree for the Multi-class Classification

Vo, T.-T., & Do, T. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-8296-7_3 · Cited by 2

Adapting the Parameters of RBF Networks Using Grammatical Evolution

Tsoulos, I. G., Tzallas, A. T., & Karvounis, E. C. (2023) · AI · doi:10.3390/ai4040054 · Cited by 2

Fault Identification of Discrete-time unknown Nonlinear Systems: A Two-dimensional Convolutional Neural Network Approach

Rangel-Carrillo, E., & Alanís, A. Y. (2023) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci52147.2023.10371910 · Cited by 2

Improving the Efficiency of College Art Teaching Based on Neural Networks

Jin, X. (2024) · International Journal of Web-Based Learning and Teaching Technologies · doi:10.4018/ijwltt.336546 · Cited by 2

Determination of droplet size from wide-angle light scattering image data using convolutional neural networks

Kirstein, T., Aßmann, S., Furat, O., Will, S., & Schmidt, V. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad2f53 · Cited by 2

Automated Detection of Spinal Lesions From CT Scans via Deep Transfer Learning

Camisa, A., Montanari, G., Testa, A., Falzetti, L., Avnet, S., Baldini, N., & Notarstefano, G. (2024) · IEEE Access · doi:10.1109/access.2024.3396999 · Cited by 2

Image Classification of Room Tidiness Using VGGNet with Data Augmentation

Fitriani, L., Latifah, A., & Cahyadiputra, M. R. (2024) · JUITA Jurnal Informatika · doi:10.30595/juita.v12i1.21204 · Cited by 2

Artificial Intelligence Approach for Classifying Images of Upper-Atmospheric Transient Luminous Events

Aguilera, A., & Manian, V. B. (2024) · Sensors · doi:10.3390/s24103208 · Cited by 2

E-MedViTR: Enhanced Vision Transformers with Registers for Biomedical Image Classification

Ahmmed, S., Rahman, T., Islam, S. M. J., Reyad, A.-M., Dey, S., Purification, J. A., & Farid, D. M. (2024) · International Conference on Electrical Engineering and Information Communication Technology · doi:10.1109/iceeict62016.2024.10534573 · Cited by 2

A Feature Based Classifier for Bangla Currency Using Deep Learning

Nahid, M., Islam, M. R., Roy, P. K., & Runti, R. (2024) · International Conference on Electrical Engineering and Information Communication Technology · doi:10.1109/iceeict62016.2024.10534568 · Cited by 2

The effect of noise removal filters on classifying different types of medical images

Atlan, F., & Pençe, İ. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104613 · Cited by 2

CLASSIFICATION OF ISOCITRATE DEHYDROGENASE (IDH) MUTATION STATUS IN GLIOMAS USING TRANSFER LEARNING

Fayyaz, M., Chaudhry, N. R., & Choudhary, R. (2024) · Pakistan Journal of Scientific Research · doi:10.57041/pjosr.v3i2.1069 · Cited by 2

Low DRAM Memory Access and Flexible Dataflow Convolutional Neural Network Accelerator based on RISC-V Custom Instruction

Chen, Y.-F., Chang, Y.-J., Chiu, C., Huang, M.-L., Liang, G.-M., Lee, C.-L., Lee, J., Hsieh, P.-Y., & Lai, W.-C. (2024) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas58744.2024.10558316 · Cited by 2

Arterial Contrast Optimization in Time-Resolved Contrast-Enhanced Cerebral MR Angiography Using Generative Neural Networks

Park, C. J., Choi, K. S., Park, J., Choi, S. H., Hwang, I., & Shin, T. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106652 · Cited by 2

Recognition and Prediction of Multi-Level Handling Complexity at Automated Terminals Based on ARIMA

Ma, Y., & Li, J. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12071201 · Cited by 2

Image Compression and Decompression Framework Based on Latent Diffusion Model for Breast Mammography

Hwang, I., & Woo, M. (2023) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci62032.2023.00221 · Cited by 3

Research on Obstructed Object Detection Algorithms in Medical X-ray images Based on Deep Learning

Liguo, L., Luhao, & Dai, F. (2024) · IEEE International Conference on Mechatronics and Automation (ICMA) · doi:10.1109/icma61710.2024.10633198 · Cited by 2

Optimized differential evolution and hybrid deep learning for superior drug-target binding affinity prediction

Bhatia, A., Sharma, M., Alabdulkreem, E. A., Alruwais, N. M., Saeed, M. K., & Yahya, A. E. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.08.074 · Cited by 3

Modular Convolutional Neural Network for Children Pneumonia Detection via X-ray Images

Kralev, K., Sotirov, S., Sotirova, E., Petrova-Geretto, E., & Petkov, D. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-70018-7_35 · Cited by 2

A Modular Deep Convolutional Neural Network for Imroving Accuracy in Prostate Biopsies

Kralev, K., Mirinchev, N., Sotirov, S., Sotirova, E., & Чолакова, З. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-70018-7_33 · Cited by 2

Plasmodium Vivax Parasite Detection using Transfer Learning Techniques

Shashikiran, S., Srinivas Babu, N., Sunitha, H. D., Mondal, S., Sadaqathulla, S., & Pramod, K. S. (2024) · Asia Pacific Conference on Innovation in Technology (APCIT) · doi:10.1109/apcit62007.2024.10673683 · Cited by 2

Malaria Cell Detection using Advanced SVM Techniques

Shashikiran, S., & Sunitha, H. D. (2024) · International Conference on Networks, Multimedia and Information Technology (NMITCON) · doi:10.1109/nmitcon62075.2024.10699300 · Cited by 2

Design of A Low-Latency General-Purpose CNN Hardware Accelerator Based on Pulsed Arrays on FPGAs

Zhang, Q., & Zhang, X. (2024) · International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) · doi:10.1109/icnc-fskd64080.2024.10702206 · Cited by 2

Environment and Person-independent Gesture Recognition with Non-static RFID Tags Leveraging Adaptive Signal Segmentation

Golipoor, S., & Sigg, S. (2024) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa61755.2024.10710733 · Cited by 2

A Simple and Provable Approach for Learning on Noisy Labeled Medical Images

Wang, N., Di, Z., He, H., Jiang, Q., & Li, X. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681274 · Cited by 2

Predicting Asthma Attacks Through AI-Powered Thermal Imaging Analysis of Breathing Patterns

Hamza, A., Himeur, Y., Amira, A., & Oulefki, A. (2024) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice62917.2024.00018 · Cited by 3

Comprehensive Overview of Computational Modeling and Artificial Intelligence in Pediatric Neurosurgery

Qiu, S., Malhotra, A. K., & Quon, J. L. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-64892-2_30 · Cited by 2

Gaussian mixture model and Bayesian convolutional neural network for abnormal musculoskeletal radiographs classification

HoangVan, X., & Van, K. T. (2024) · Evolutionary Intelligence · doi:10.1007/s12065-024-00993-w · Cited by 2

FPGA Implementation of an Image Classifier Using Pipelined FFT Architecture

Hai, S., & Reddy, T. R. (2024) · IEEE Embedded Systems Letters · doi:10.1109/les.2024.3500020 · Cited by 2

Mobile-Based Skin Cancer Classification System Using Convolutional Neural Network

Aljundi, I. I., Novaliendry, D., Hendriyani, Y., & Syafrijon, S. (2024) · Data & Metadata · doi:10.56294/dm2024.649 · Cited by 2

A Study on an Improved ResNet Model for Image Classification

Liu, Y., Wang, W., & He, W. (2024) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei64163.2024.10906260 · Cited by 2

Neural Network-Based Machine Learning Approaches for Alzheimer’s Disease Prediction

Soundarapandian, R. (2024) · Asian Conference on Intelligent Technologies (ACOIT) · doi:10.1109/acoit62457.2024.10941124 · Cited by 2

Machine learning driven biomarker selection for medical diagnosis

Bavikadi, D., Agarwal, A., Ganta, S., Chung, Y., Song, L., Qiu, J., & Shakarian, P. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322620 · Cited by 2

An Effective Image Denoising Model Using Improved Deep Learning Techniques with Optimization Algorithm

Mythili, S., Sivaraju, S. S., Anuradha, T., & Sivarajan, S. (2025) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001425520184 · Cited by 2

RadientFusion‐XR: A Hybrid LBP–HOG Model for COVID‐19 Detection Using Machine Learning

Greeshma, K. V., & Gripsy, J. V. (2025) · Biotechnology and Applied Biochemistry · doi:10.1002/bab.70020 · Cited by 2

Artificial Intelligence Networks Combining Histopathology and Machine Learning Can Extract Axon Pathology in Autism Spectrum Disorder

Yazdanbakhsh, A., Dang, K. T. M., Kuang, K., Lian, T., Liu, X., Xie, S., & Zikopoulos, B. (2025) · Autism Research · doi:10.1002/aur.70135 · Cited by 2

Recognition of Tomographic Images in the Diagnosis of Stroke

Kalmutskiy, K., Тулупов, А. А., & Berikov, V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68821-9_16 · Cited by 2

Texture improvement for human shape estimation from a single image

Escribano, J. G., Rauno, S., Swaminathan, A., Smyth, D., & Smolić, A. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/soww6683 · Cited by 2

Pneumonia Detection from Chest X-ray Images Using Transfer Learning by Fusing the Features of Pre-trained Xception and VGG16 Networks

Shafi, A. S. M. A., Maruf, M. M. H., & Das, S. (2022) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit57492.2022.10054672 · Cited by 2

METHOD AND SOFTWARE COMPONENT MODEL FOR SKIN DISEASE DIAGNOSIS

Льовкін, В. М., Субботин, С. А., Oliinyk, А., & Myronenko, N. V. (2023) · Radio Electronics Computer Science Control · doi:10.15588/1607-3274-2023-1-4 · Cited by 2

Klasifikasi American Sign Language Menggunakan Convolutional Neural Network

Israldi, T., Haerani, E., Sanjaya, S., & Syafria, F. (2022) · Building of Informatics Technology and Science (BITS) · doi:10.47065/bits.v4i3.2570 · Cited by 2

Predicting DNA Damage in Fluorescent Imaging

McDonough, D. J., & Vadivazhagu, S. (2020) · Digital WPI · Cited by 2

An Epileptic Seizure Prediction Model based on a Simulation Block and a Pretrained ResNet

Jiang, Y., Yang, L., & Lu, Y. (2020) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei51763.2020.9263591 · Cited by 2

A Comparative Analysis of Pneumonia Detection Using Various Models of Transfer Learning

Narayanan, B., Kuriakose, V. A. A., & Sreekumar, K. P. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-3675-2_10 · Cited by 2

A Review of CNN on Medical Imaging to Diagnose COVID-19 Infections

Clark, S., Kamalinejad, E., Magpantay, C., Sahota, S., Zhong, J., & Hu, Y. (2021) · EPiC series in computing · doi:10.29007/6xsd · Cited by 2

Chain Graph Explanation of Neural Network Based on Feature-Level Class Confusion

Hwang, H., Park, E., & Shin, J. (2022) · Applied Sciences · doi:10.3390/app12031523 · Cited by 2

Faulty RJ45 connectors detection on radio base station using deep learning

Dantas, M. S. M., Leuchtenberg, P. H. D., de Souza, G. F. R., Bezerra, D., de Souza, R. G., Lins, S., Kelner, J., & Sadok, D. F. H. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12694-6 · Cited by 2

Unsupervised Scale-Invariant Multispectral Shape Matching

Pazi, I., Ginzburg, D., & Raviv, D. (2022) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.56541/vhmq4826 · Cited by 5

CNN Based Transfer Learning for Malaria Parasite Detection Using Thin-Blood Smear Images

Swastika, W., Pradana, B. J., Widodo, R. B., Sitepu, R., & Putra, G. G. S. (2022) · International Conference on Sustainable Information Engineering and Technology (SIET) · doi:10.1145/3568231.3568243 · Cited by 2

A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health

Alam, M. M., Alam, M. M., Moinuddin, M., Ahmad, M. T., Hakami, J. W., Chaudhary, A. A., Khan, A. I., & Mohd, T. K. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.032118 · Cited by 2

Pneumonia Detection Using Deep Learning Based Feature Extraction and Machine Learning

Shekar, B. H., Mannan, S., & Hailu, H. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-31417-9_45 · Cited by 2

A Machine Learning Application for Medical Image Analysis Using Deep Convolutional Neural Networks (CNNs) and Transfer Learning Models for Pneumonia Detection

Shirwaikar, R. D. (2024) · Journal of Electrical Systems · doi:10.52783/jes.2591 · Cited by 2

Automatic Detection of Pneumonia from Chest X-Rays Using Deep Learning

Nath, M., & Choudhury, C. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-6315-7_14 · Cited by 12

A pneumonia classification accuracy enhancement scheme based on data augmentation, data preprocessing, and transfer learning

Mao, T., Li, T., & Zhang, Y. (2023) · International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID) · doi:10.1117/12.2674701 · Cited by 2

Pneumonia Detection using X-Ray Images with Deep Learning

Magar, H., Patil, S., Waykole, S. R., Sandikar, S. D., & Parakh, N. D. (2020) · International Journal of Computer Applications Technology and Research · doi:10.7753/ijcatr0905.1004 · Cited by 3

Pneumonia Classification Using VGG19 Transfer Learning And Data Augmentation Across Diverse Chest X-Ray Datasets

Kalaiselvi, K. (2024) · African Journal of Biomedical Research · doi:10.53555/ajbr.v27i3s.2634 · Cited by 1