Computer-aided diagnosis in medical imaging: Historical review, current status and future potential

Randomised trial of effect of alendronate on risk of fracture in women with existing vertebral fractures

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Early Lung Cancer Action Project: overall design and findings from baseline screening

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Effect of Oral Alendronate on Bone Mineral Density and the Incidence of Fractures in Postmenopausal Osteoporosis

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ROC Methodology in Radiologic Imaging

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A randomized trial of nasal spray salmon calcitonin in postmenopausal women with established osteoporosis: the prevent recurrence of osteoporotic fractures study

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Mass screening for lung cancer with mobile spiral computed tomography scanner

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CT Screening for Lung Cancer

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Some Practical Issues of Experimental Design and Data Analysis in Radiological ROC Studies

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Lung Field Segmentation in Chest X-rays: A Deformation-Tolerant Procedure Based on the Approximation of Rib Cage Seed Points

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Classification Approach for Breast Cancer Detection Using Back Propagation Neural Network

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Enhancing YOLOv11 with Large Kernel Attention and Multi-Scale Fusion for Accurate Small and Multi-Lesion Bone Tumor Detection in Radiographs

Chen, S., Peng, Y., Liu, Y., Wang, P., & Liu, T. (2025) · Diagnostics · doi:10.3390/diagnostics15161988 · Cited by 14

A Bag-of-Tasks approach to speed up the lung nodules retrieval in the BigData age

Oliveira, M. C., & Ferreira, J. R. (2013) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2013.6720753 · Cited by 14

Breast Tissue Density Classification Using Wavelet-Based Texture Descriptors

Virmani, J., & Kriti, N. A. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-81-322-2526-3_56 · Cited by 14

Feasibility of Using Improved Convolutional Neural Network to Classify BI‐RADS 4 Breast Lesions: Compare Deep Learning Features of the Lesion Itself and the Minimum Bounding Cube of Lesion

Sheng, M., Tang, W., Tang, J., Zhang, M., Gong, S., & Xing, W. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/4430886 · Cited by 14

An integrated method for detecting lung cancer via CT scanning via optimization, deep learning, and IoT data transmission

Karimullah, S., Khan, M., Shaik, F., Alabduallah, B. I., & Almjally, A. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1435041 · Cited by 17

Acute ischemic stroke detection using wavelet based fusion of CT and MRI images

Mirajkar, P. R., Bhagwat, K. A., Singh, A., & Ashalatha, M. E. (2015) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2015.7275761 · Cited by 14

Automatic image segmentation and classification based on direction texton technique for hemolytic anemia in thin blood smears

Chen, H.-M., Tsao, Y., & Tsai, S.-N. (2013) · Machine Vision and Applications · doi:10.1007/s00138-013-0585-y · Cited by 14

Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior

Cai, Q., Kang, J., & Yu, T. (2019) · Bayesian Analysis · doi:10.1214/18-ba1142 · Cited by 14

Automatic Classification of Benign and Malignant Breast Tumors in Ultrasound Image with Texture and Morphological Features

Wei, M., Du, Y., Wu, X., & Zhu, J. (2019) · IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID) · doi:10.1109/icasid.2019.8925194 · Cited by 14

Crowdsourcing human-based computation for medical image analysis: A systematic literature review

Petrović, N., Moyà-Alcover, G., Varona, J., & Jaume-i-Capó, A. (2020) · Health Informatics Journal · doi:10.1177/1460458220907435 · Cited by 14

Automatic detection of intracranial aneurysms in 3D-DSA based on a Bayesian optimized filter

Hu, T., Yang, H., Ni, W., Lei, Y., Jiang, Z., Shi, K., Yu, J., Gu, Y., & Wang, Y. (2020) · BioMedical Engineering OnLine · doi:10.1186/s12938-020-00817-9 · Cited by 14

Tear Film Classification in Interferometry Eye Images Using Phylogenetic Diversity Indexes and Ripley's K Function

da Cruz, L. B., Souza, J. C., de Paiva, A. C., de Almeida, J. D. S., Bráz, G., Aires, K. R. T., Silva, A. C., & Gattass, M. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3026940 · Cited by 14

Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network

Zhang, S., Zhi, L., & Zhou, T. (2020) · BioMed Research International · doi:10.1155/2020/6687733 · Cited by 14

Hepatocellular carcinoma histopathological images grading with a novel attention-sharing hybrid network based on multi-feature fusion

Zhang, J., Qiu, S., Li, Q., Zhou, C., Hu, Z., Weng, J., Sheng, X., Dong, Q., & Ren, N. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105126 · Cited by 14

Morphological transfer learning based brain tumor detection using YOLOv5

Pandey, S. K., & Bhandari, A. K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17367-6 · Cited by 14

Automatic Detection of Lung and Liver Lesions in 3-D Positron Emission Tomography Images: A Pilot Study

Lartizien, C., Marache-Francisco, S., & Prost, R. (2012) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.2011.2180923 · Cited by 14

Computer-aided breast cancer histopathological diagnosis: Comparative analysis of three DTOCS-based features: SW-DTOCS, SW-WDTOCS and SW-3-4-DTOCS

Pöllänen, I., Braithwaite, B., Ikonen, T., Niska, H., Haataja, K., Toivanen, P., & Tolonen, T. T. (2014) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2014.7001986 · Cited by 14

Computer-based segmentation, change detection and quantification for lesions in multiple sclerosis

Göçeri, E., & Songul, C. (2017) · International Conference on Computer Science and Engineering (UBMK) · doi:10.1109/ubmk.2017.8093371 · Cited by 14

Lung Disease Classification using GLCM and Deep Features from Different Deep Learning Architectures with Principal Component Analysis

Ming, J. T. C., Noor, N. M., Rijal, O. M., Kassim, R. M., & Yunus, A. (2018) · International Journal of Integrated Engineering · doi:10.30880/ijie.2018.10.07.008 · Cited by 14

Intuitionistic Fuzzy Clustering Method with Spatial Information for MRI Image Segmentation

Kumar, D., Agrawal, R. K., & Kirar, J. S. (2019) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2019.8858865 · Cited by 14

[Research progress of computer-aided diagnosis in cancer based on deep learning and medical imaging].

Chen, S., Liu, W., Qin, J., Chen, L., Bin, G., Zhou, Y., Wang, T., & Huang, B. (2017) · PubMed Central · doi:10.7507/1001-5515.201609047 · Cited by 14

Particle swarm optimization‐based liver disorder ultrasound image classification using multi‐level and multi‐domain features

Krishnamurthy, R. K., Sudhakar, R., & Kadhar, K. M. A. (2020) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22518 · Cited by 14

Boosting Breast Cancer Classification from Microscopic Images Using Attention Mechanism

Ukwuoma, C. C., Urama, G. C., Qin, Z., Heyat, M. B. B., Khan, H. M., Akhtar, F., Masadeh, M., Ibegbulam, C. S., Fiasam, L. D., & AlShorman, O. (2022) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa54658.2022.9765013 · Cited by 14

Cellular Level Based Deep Learning Framework for Early Detection of Dysplasia in Oral Squamous Epithelium

Gupta, R. K., Kaur, M., & Manhas, J. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-29407-6_12 · Cited by 14

An X-ray CAD system with ribcage suppression for improved detection of lung lesions

Horváth, Á., Orban, G., Horváth, Á., & Horváth, G. (2013) · Periodica Polytechnica Electrical Engineering · doi:10.3311/ppee.2079 · Cited by 14

Study and development of a Computer-Aided Diagnosis system for classification of chest x-ray images using convolutional neural networks pre-trained for ImageNet and data augmentationPreprint

Vianna, V. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.00839 · Cited by 17

Long-Bone Fracture Detection using Artificial Neural Networks based on Contour Features of X-ray ImagesPreprint

Yang, A. Y., & Cheng, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07897 · Cited by 14

GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathology Image Classification.Preprint

Chen, H., Li, C., Li, X., Hu, W., Li, Y., Liu, W., Sun, C., Yao, Y., & Grzegorzek, M. (2021) · arXiv (Cornell University) · Cited by 14

Revolutionizing emergency care: an overview of the transformative role of artificial intelligence in diagnosis, triage, and patient management

Dehbaghi, H. A., & Khoshgard, K. (2025) · International Journal of Emergency Medicine · doi:10.1186/s12245-025-01049-1 · Cited by 16

Artificial Intelligence in Diagnostic Medical Image Processing for Advanced Healthcare Applications

Kalita, A. J., Boruah, A., Das, T., Mazumder, N., Jaiswal, S. K., Zhuo, G., Gogoi, A., Kakoty, N. M., & Kao, F. (2024) · Biological and medical physics, biomedical engineering · doi:10.1007/978-981-97-5345-1_1 · Cited by 13

Cross-ViT based benign and malignant classification of pulmonary nodules

Zhu, Q., & Fei, L. (2025) · PLoS ONE · doi:10.1371/journal.pone.0318670 · Cited by 13

Advancing respiratory disease diagnosis: A deep learning and vision transformer-based approach with a novel X-ray dataset

AlGhadhban, A., Ramadan, R. Α., & Alazmi, M. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110501 · Cited by 13

Bilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms

Celaya-Padilla, J. M., Martínez‐Torteya, A., Rodríguez-Rojas, J., Galván-Tejada, J. I., Treviño, V., & Tamez‐Peña, J. G. (2015) · BioMed Research International · doi:10.1155/2015/231656 · Cited by 13

Automatic grade classification of Barretts Esophagus through feature enhancement

Ghatwary, N., Ahmed, A. A., Ye, X., & Jalab, H. A. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2250364 · Cited by 13

A proposed computer-aided diagnosis system for Parkinson's disease classification using123I-FP-CIT imaging

Brahim, A., Khedher, L., Górriz, J. M., Ramı́rez, J., Toumi, H., Lespessailles, É., Jennane, R., & Hassouni, M. E. (2017) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip.2017.8075510 · Cited by 13

Deep Neural Networks for Diagnosis of Osteoporosis: A Review

Wani, I. M., & Arora, S. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-29407-6_6 · Cited by 13

A Review on Multiple Approaches to Medical Image Retrieval System

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Computer aided detection of tuberculosis using two classifiers

Ibrahim, A. U., Al-Turjman, F. M., Özsöz, M., & Serte, S. (2022) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2021-0310 · Cited by 13

Improved pulmonary nodule classification utilizing lung parenchyma texture features

Dilger, S. K. N., Judisch, A., Uthoff, J., Hammond, E., Newell, J. D., & Sieren, J. C. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2081397 · Cited by 13

Decision support in heart failure through processing of electro- and echocardiograms

Chiarugi, F., Colantonio, S., Emmanouilidou, D., Martinelli, M., Moroni, D., & Salvetti, O. (2010) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2010.05.001 · Cited by 13

Computer-Aided Diagnosis for Detection of Lacunar Infarcts on MR Images: ROC Analysis of Radiologists’ Performance

Uchiyama, Y., Asano, T., Kato, H., Hara, T., Kanematsu, M., Hoshi, H., Iwama, T., & Fujita, H. (2012) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-011-9444-4 · Cited by 13

Towards a Repository for Standardized Medical Image and Signal Case Data Annotated with Ground Truth

Deserno, T. M., Petra Welter, A., & Horsch, A. (2011) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-011-9428-4 · Cited by 13

Investigation of Optimal Use of Computer-Aided Detection Systems

Paquerault, S., Hardy, P. T., Wersto, N., Chen, J., & Smith, R. C. (2010) · Academic Radiology · doi:10.1016/j.acra.2010.04.010 · Cited by 13

PACS: Sistemas de Arquivamento e Distribuição de Imagens

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Medical image super-resolution with non-local embedding sparse representation and improved IBP

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A Computer-Aided Detection System for Digital Chest Radiographs

de Gea, J. M. C., Garcı́a-Mateos, G., Fernández‐Alemán, J. L., & Hernández-Hernández, J. L. (2016) · Journal of Healthcare Engineering · doi:10.1155/2016/8208923 · Cited by 13

Integrating 3D image descriptors of margin sharpness and texture on a GPU-optimized similar pulmonary nodule retrieval engine

Ferreira, J. R., Oliveira, M. C., & de Azevedo‐Marques, P. M. (2016) · The Journal of Supercomputing · doi:10.1007/s11227-016-1818-4 · Cited by 13

Automated segmentation of brain lesion based on diffusion-weighted MRI using a split and merge approach

Saad, N. M., Abu–Bakar, S. A. R., Muda, S., & Mokji, M. M. (2010) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes.2010.5742284 · Cited by 13

Segmentation method for medical image based on improved GrabCut

Lu, Y., Jiang, J., Qi, M., Zhan, S., & Yang, J. (2017) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22242 · Cited by 13

Multimodality, interactivity, and crowdsourcing for document transcription

Granell, E., Romero, V., & Martínez-Hinarejos, C.-D. (2018) · Computational Intelligence · doi:10.1111/coin.12169 · Cited by 13

Automated glaucoma detection using retinal layers segmentation and optic cup‐to‐disc ratio in optical coherence tomography images

Ramzan, A., Akram, M. U., Shaukat, A., Khawaja, S. G., Yasin, U. U., & Butt, W. H. (2018) · IET Image Processing · doi:10.1049/iet-ipr.2018.5396 · Cited by 13

Role of the Medical Physicist in the Health Care Artificial Intelligence Revolution

Sensakovic, W. F., & Mahesh, M. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2018.09.022 · Cited by 13

Computer aided diagnostic system based on SVM and K harmonic mean based attribute weighting method

Srivastava, A. K., Kumar, Y., & Singh, P. (2020) · Obesity Medicine · doi:10.1016/j.obmed.2020.100270 · Cited by 13

3D conditional generative adversarial network‐based synthetic medical image augmentation for lung nodule detection

Bu, T., Yang, Z., Jiang, S., Zhang, G., Zhang, H., & Wei, L. (2020) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22511 · Cited by 13

Computer Aided Diagnosis for lung tuberculosis identification based on thoracic X-ray

Rohmah, R. N., Susanto, A., Soesanti, I., & Tjokronagoro, M. (2013) · doi:10.1109/iciteed.2013.6676214 · Cited by 13

A Sparse Representation Based Method to Classify Pulmonary Patterns of Diffuse Lung Diseases

Zhao, W., Xu, R., Hirano, Y., Tachibana, R., & Kido, S. (2015) · Computational and Mathematical Methods in Medicine · doi:10.1155/2015/567932 · Cited by 13

A CAD System for Hemorrhagic Stroke

Nowinski, W. L., Qian, G., & Hanley, D. F. (2014) · The Neuroradiology Journal · doi:10.15274/nrj-2014-10080 · Cited by 13

Heart Sound Segmentation for digital stethoscope integration

Hedayioglu, F. D. L. (2011) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · doi:10.34626/d7d8-3q69 · Cited by 13

Use of Transfer Learning and Wavelet Transform for Breast Cancer DetectionPreprint

Rasheed, A., Younis, M. S., Qadir, J., & Bilal, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03602 · Cited by 13

Revolutionizing Alzheimer’s detection: an advanced telemedicine system integrating Internet-of-Things and convolutional neural networks

Massoud, M. A., El-Bouridy, M. E., & Ahmed, W. A. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09859-9 · Cited by 12

Intra-Individual Reproducibility of Automated Abdominal Organ Segmentation—Performance of TotalSegmentator Compared to Human Readers and an Independent nnU-Net Model

Abel, L., Wasserthal, J., Meyer, M. T., Vosshenrich, J., Yang, S., Donners, R., Obmann, M. M., Boll, D. T., Merkle, E. M., Breit, H., et al. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01265-w · Cited by 12

FUSCANet: Enhancing Skin Disease Classification Through Feature Fusion and Spatial-Channel Attention Mechanisms

Liu, Q., Wang, X., Liu, H., Zang, X., Li, L., Ji, Z., & Ганчев, И. (2025) · IEEE Access · doi:10.1109/access.2025.3577740 · Cited by 12

Leveraging pretrained vision‐language model for enhanced breast cancer diagnosis with multi‐view mammography

Chen, X., Li, Y., Hu, M., Salari, E., Chen, X., Qiu, R. L. J., Zheng, B., & Yang, X. (2026) · Medical Physics · doi:10.1002/mp.70261 · Cited by 21

Representation of Lesion Similarity by Use of Multidimensional Scaling for Breast Masses on Mammograms

Muramatsu, C., Nishimura, K., Endo, T., Oiwa, M., Shiraiwa, M., Doi, K., & Fujita, H. (2013) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-012-9569-0 · Cited by 12

Post-mortem 1.5T MR quantification of regular anatomical brain structures

Zech, W., Hottinger, A.-L., Schwendener, N., Schuster, F., Persson, A., Warntjes, M. J. B., & Jackowski, C. (2016) · International Journal of Legal Medicine · doi:10.1007/s00414-016-1318-3 · Cited by 12

Development of a Reference Image Collection Library for Histopathology Image Processing, Analysis and Decision Support Systems Research

Kostopoulos, S. A., Ravazoula, P., Asvestas, P. A., Kalatzis, I. K., Xenogiannopoulos, G., Cavouras, D. A., & Glotsos, D. T. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-9947-8 · Cited by 12

Segmentation of Liver Tumor for Computer Aided Diagnosis

Anwar, S. M., Awan, S., Yousaf, S., & Majid, M. (2018) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes.2018.8626682 · Cited by 12

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

Identifying Parkinson’s Disease Through the Classification of Audio Recording Data

Bielby, J., Kühn, S., Colreavy‐Donnelly, S., Caraffini, F., O’Connor, S., & Anastassi, Z. A. (2020) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec48606.2020.9185915 · Cited by 12

Facial Reconstruction: A Systematic Review of Current Image Acquisition and Processing Techniques

Tarassoli, S. P., Shield, M. E., Allen, R. S., Jessop, Z. M., Dobbs, T. D., & Whitaker, I. S. (2020) · Frontiers in Surgery · doi:10.3389/fsurg.2020.537616 · Cited by 12

Yapay Zekâ ve Tıbbi Görüntüleme Teknolojilerine Genel Bakış

Atlan, F., & Pençe, İ. (2021) · Acta Infologica · doi:10.26650/acin.813736 · Cited by 12

Deep Learning and Binary Relevance Classification of Multiple Diseases using Chest X-Ray images

Blais, M.-A., & Akhloufi, M. A. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9629846 · Cited by 12

Identification of glomerulosclerosis using IBM Watson and shallow neural networks

Pesce, F., Albanese, F., Mallardi, D., Rossini, M., Pasculli, G., Suavo-Bulzis, P., Granata, A. R. M., Brunetti, A., Cascarano, G. D., Bevilacqua, V., et al. (2022) · Journal of Nephrology · doi:10.1007/s40620-021-01200-0 · Cited by 12

Clinical Application of Computer-Aided Diagnosis for Breast Ultrasonography: Factors That Lead to Discordant Results in Radial and Antiradial Planes

Zhu, Y., Zhan, W., Jia, X., Liu, J., & Zhou, J. (2022) · Cancer Management and Research · doi:10.2147/cmar.s348463 · Cited by 12

A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms

Loizidou, K., Skouroumouni, G., Nikolaou, C., & Pitris, C. (2022) · Tomography · doi:10.3390/tomography8060241 · Cited by 12

Deep CNN and LSTM Architecture-Based Approach for COVID-19 Detection

Mehta, S., Rastogi, U., & Dwivedi, P. (2023) · International Conference on Signal Processing and Integrated Networks (SPIN) · doi:10.1109/spin57001.2023.10117454 · Cited by 12

Comparing Performance of the CADstream and the DynaCAD Breast MRI CAD Systems

Pan, J., Doğan, B. E., Çarkaci, S., Santiago, L., Arribas, E. M., Cantor, S. B., Wei, W., Stafford, R. J., & Whitman, G. J. (2013) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-013-9602-y · Cited by 12

Gaussian mixture modeling for statistical analysis of features of high-resolution CT images of diffuse pulmonary diseases

ALMEIDA, E. S. D., Rangayyan, R. M., & de Azevedo‐Marques, P. M. (2015) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2015.7145162 · Cited by 12

Improved Differential Diagnosis of Breast Masses on Ultrasonographic Images with a Computer-Aided Diagnosis Scheme for Determining Histological Classifications

Kashikura, Y., Nakayama, R., Hizukuri, A., Noro, A., Nohara, Y., Nakamura, T., Ito, M., Kimura, H., Yamashita, M., Hanamura, N., et al. (2013) · Academic Radiology · doi:10.1016/j.acra.2012.11.007 · Cited by 12

Accuracy of computer-assisted image analysis in the diagnosis of maxillofacial radiolucent lesions: A systematic review and meta-analysis

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Glioma Brain Tumors Diagnosis and Classification in MR Images based on Convolutional Neural Networks

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