Deep Learning Approach for Medical Image Analysis
Open accessAbstract
Localization of region of interest (ROI) is paramount to the analysis of medical images to assist in the identification and detection of diseases. In this research, we explore the application of a deep learning approach in the analysis of some medical images. Traditional methods have been restricted due to the coarse and granulated appearance of most of these images. Recently, deep learning techniques have produced promising results in the segmentation of medical images for the diagnosis of diseases. This research experiments on medical images using a robust deep learning architecture based on the Fully Convolutional Network‐ (FCN‐) UNET method for the segmentation of three samples of medical images such as skin lesion, retinal images, and brain Magnetic Resonance Imaging (MRI) images. The proposed method can efficiently identify the ROI on these images to assist in the diagnosis of diseases such as skin cancer, eye defects and diabetes, and brain tumor. This system was evaluated on publicly available databases such as the International Symposium on Biomedical Imaging (ISBI) skin lesion images, retina images, and brain tumor datasets with over 90% accuracy and dice coefficient.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Deep learning and reinforcement learning are powerful AI techniques for medical data analysis.
“Deep learning and reinforcement learning are two powerful AI techniques for medical data analysis [17,30].”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [30]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Convolutional neural networks: an overview and application in radiology
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682 · cited 1× · see citing works
“(ese approaches are however well constrained with scarcely accessible labeled datasets for training the deep learning models for effective performance [3].”
BackgroundRidge-based vessel segmentation in color images of the retina
Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.825627 · Cited by 4,202 · see citing works
Codella, N. C. F., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., Kittler, H., et al. (2018) · 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) · doi:10.1109/isbi.2018.8363547 · Cited by 2,620 · see citing works
Automatic Segmentation of MR Brain Images With a Convolutional Neural Network
Moeskops, P., Viergever, M. A., Mendrik, A. M., de Vries, L. S., Benders, M. J. N. L., & Isgum, I. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2548501 · Cited by 861 · see citing works
Cruz-Roa, Á., Basavanhally, A. N., Gonzalez, F. A., Gilmore, H. L., Feldman, M. D., Ganesan, S., Shih, N. N. C., Tomaszewski, J., & Madabhushi, A. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2043872 · Cited by 613 · see citing works
Deep Learning and Medical Diagnosis: A Review of Literature
Bakator, M., & Radosav, D. (2018) · Multimodal Technologies and Interaction · doi:10.3390/mti2030047 · Cited by 509 · see citing works
Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks
Al‐masni, M. A., Al–antari, M. A., Choi, M., Han, S., & Kim, T. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.05.027 · Cited by 484 · see citing works
Adegun, A. A., & Viriri, S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09865-y · Cited by 331 · see citing works
Medical breast ultrasound image segmentation by machine learning
Xu, Y. X., Wang, Y., Yuan, J., Cheng, Q., Wang, X., & Carson, P. L. (2018) · Ultrasonics · doi:10.1016/j.ultras.2018.07.006 · Cited by 280 · see citing works
RIC-Unet: An Improved Neural Network Based on Unet for Nuclei Segmentation in Histology Images
Zeng, Z., Xie, W., Zhang, Y., & Lu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2896920 · Cited by 280 · see citing works
Deep Learning-Based System for Automatic Melanoma Detection
Adegun, A. A., & Viriri, S. (2019) · IEEE Access · doi:10.1109/access.2019.2962812 · Cited by 232 · see citing works
Adegun, A. A., & Viriri, S. (2020) · IEEE Access · doi:10.1109/access.2020.3016651 · Cited by 212 · see citing works
Step-wise integration of deep class-specific learning for dermoscopic image segmentation
Bi, L., Kim, J., Ahn, E., Kumar, A., Feng, D. D., & FULHAM, M. J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.08.001 · Cited by 194 · see citing works
A survey and taxonomy of the fuzzy signature-based Intrusion Detection Systems
Masdari, M., & Khezri, H. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106301 · Cited by 192 · see citing works
Deep learning and its application to medical image segmentationPreprint
Roth, H. R., Shen, C., Oda, H., Oda, M., Hayashi, Y., Misawa, K., & Mori, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.08691 · Cited by 136 · see citing works
Breast ultrasound lesions recognition: end-to-end deep learning approaches
Yap, M. H., Goyal, M., Osman, F. M., Martí, R., Denton, E., Juette, A., & Reyer Zwiggelaar, A. (2018) · Journal of medical imaging · doi:10.1117/1.jmi.6.1.011007 · Cited by 105 · see citing works
Adaptive Image Contrast Enhancement by Computing Distances into a 4-Dimensional Fuzzy Unit Hypercube
Versaci, M., Morabito, F. C., & Angiulli, G. (2017) · IEEE Access · doi:10.1109/access.2017.2776349 · Cited by 48 · see citing works
Khagi, B., & Kwon, G. (2018) · Journal of Healthcare Engineering · doi:10.1155/2018/3640705 · Cited by 47 · see citing works
Zhou, X., Yamada, K., Takayama, R., Zhou, X., Hara, T., Fujita, H., Wang, S., & Kojima, T. (2018) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2295178 · Cited by 43 · see citing works
Yauney, G., Angelino, K., Edlund, D., & Shah, P. P. (2017) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2017.00-37 · Cited by 24 · see citing works
An Enhanced Deep Learning Framework for Skin Lesions Segmentation
Adegun, A. A., & Viriri, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28377-3_34 · Cited by 22 · see citing works
A Deep Convolutional Encoder-Decoder Architecture for Retinal Blood Vessels Segmentation
Adegun, A. A., Adebiyi, M. O., Oluwatobi, A. N., Ogundokun, R. O., Kayode, A. A., & Oladele, T. O. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-24308-1_15 · Cited by 16 · see citing works
Hashmani, N., & Hashmani, S. (2017) · Clinical ophthalmology · doi:10.2147/opth.s150617 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- M. Bellver, K.-K. Maninis, J. Pont-Tuset, X. Giró-i-Nieto, J. Torres, and L. Van Gool, "Detection-aided liver lesion segmentation using deep learning," 2017, http://arxiv.org/abs/ 1711.11069.
- V. Badrinarayanan, A. Handa, and R. Cipolla, "Segnet: a deep convolutional encoder-decoder architecture for robust se- mantic pixel-wise labeling," 2015, http://arxiv.org/abs/1505. 07293.
Cited by
Sorted by citations
Badidi, E. (2023) · Future Internet · doi:10.3390/fi15110370 · Cited by 113 · see citing works
Ogundokun, R. O., Misra, S., Akinrotimi, A. O., & Oğul, H. (2023) · Sensors · doi:10.3390/s23020656 · Cited by 67 · see citing works
Ogundokun, R. O., Li, A., Babatunde, R. S., Umezuruike, C., Sadiku, P. O., Abdulahi, A. T., & Babatunde, A. N. (2023) · Bioengineering · doi:10.3390/bioengineering10080979 · Cited by 63 · see citing works
Multi-Model Medical Image Segmentation Using Multi-Stage Generative Adversarial Networks
Khaled, A., Han, J.-J., & Ghaleb, T. A. (2022) · IEEE Access · doi:10.1109/access.2022.3158342 · Cited by 32 · see citing works
Eali, S. N. J., Bhattacharyya, D., Nakka, T. R., & Hong, S.-P. (2022) · Traitement du signal · doi:10.18280/ts.390203 · Cited by 29 · see citing works
StynMedGAN: Medical images augmentation using a new GAN model for improved diagnosis of diseases
Wali, A., Ahmad, M., Naseer, A., Tamoor, M., & Gilani, S. A. M. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-223996 · Cited by 25 · see citing works
Islam, M., Zunair, H., & Mohammed, N. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108317 · Cited by 21 · see citing works
Li, T., Zhang, B., Lv, H., Hu, S., Xu, Z., & Tuergong, Y. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19095199 · Cited by 17 · see citing works
Diagnosis of Gallbladder Disease Using Artificial Intelligence: A Comparative Study
Obaid, A. M., Turki, A., Bellâaj, H., & Ksantini, M. (2024) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-024-00431-w · Cited by 16 · see citing works
Deep learning model-based segmentation of medical diseases from MRI and CT images
Murmu, A., & Kumar, P. (2021) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon54134.2021.9707278 · Cited by 14 · see citing works
Liver Tumour Segmentation based on ResNet Technique
Sirco, A., Almisreb, A. A., Tahir, N. M., & Bakri, J. (2022) · IEEE International Conference on Control System, Computing and Engineering · doi:10.1109/iccsce54767.2022.9935636 · Cited by 13 · see citing works
Explainable Artificial Intelligence with Scaling Techniques to Classify Breast Cancer Images
Adelodun, A. B., Ogundokun, R. O., Yekini, A. O., Awotunde, J. B., & Timothy, C. C. (2023) · Springer eBooks · doi:10.1007/978-3-031-38036-5_6 · Cited by 12 · see citing works
Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images
Ogundokun, R. O., Awotunde, J. B., Akande, H. B., Lee, C., & Imoize, A. L. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.052153 · Cited by 11 · see citing works
Future of Electronic Healthcare Management: Blockchain and Artificial Intelligence Integration
Verma, P., Rao, C. M., Chapalamadugu, P. K., Tiwari, R., & Upadhyay, S. (2024) · Blockchain technologies · doi:10.1007/978-981-97-1249-6_9 · Cited by 12 · see citing works
Pediatric pancreas segmentation from MRI scans with deep learning
Keleş, E., Yazol, M., Durak, G., Hong, Z., Aktas, H. E., Zhang, Z., Peng, L., Susladkar, O., Guzelyel, N., Boyunaga, O. L. K., et al. (2025) · Pancreatology · doi:10.1016/j.pan.2025.06.006 · Cited by 9 · see citing works
Wei, Y., Jagtap, J. M. M., Singh, Y., Khosravi, B., Cai, J. C., Gunter, J. L., & Erickson, B. J. (2024) · American Journal of Neuroradiology · doi:10.3174/ajnr.a8544 · Cited by 8 · see citing works
Assessment of Machine Learning Classifiers for Heart Diseases Discovery
Ogundokun, R. O., Misra, S., Sadiku, P. O., & Adeniyi, J. K. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-030-95947-0_31 · Cited by 7 · see citing works
Xu, N., Wang, X., Xu, Y., Zhao, T., & Li, X. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/9012709 · Cited by 7 · see citing works
Feng, L., Chen, C., Wang, L., Zhang, J., Li, Y., Yang, T. H., Fan, Y., Liu, P., Sun, P., & Huang, F. L. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106223 · Cited by 6 · see citing works
Metastatic Breast Cancer Detection Using Deep Learning Algorithms: A Systematic Review
Adedayo-Ajayi, V. O., Ogundokun, R. O., Aderemi, E. T., Adebiyi, M. O., & Adebiyi, A. A. (2023) · International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG) · doi:10.1109/seb-sdg57117.2023.10124547 · Cited by 6 · see citing works
Machine Learning for Medical Image Analysis: A Survey
Fettah, A., Menassel, R., & Gattal, A. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-35248-5_14 · Cited by 6 · see citing works
Classification of Citrus Canker and Black Spot Diseases using a Deep Learning based Approach
Adeem, G., Rehman, S. U., & Ahmad, S. (2022) · VFAST Transactions on Software Engineering · doi:10.21015/vtess.v15i3.976 · Cited by 6 · see citing works
Early Detection of Fake News from Social Media Networks Using Computational Intelligence Approaches
Ogundokun, R. O., Arowolo, M. O., Misra, S., & Oladipo, I. D. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-90087-8_4 · Cited by 6 · see citing works
The Application of Deep Learning Model in Recruitment Decision
Wang, Y., & Zhu, Z. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/9645830 · Cited by 5 · see citing works
Enhanced Skin Cancer Classification: Proposed Efficient Combination Model in CNN Architectures
Benchari, M., Bayoumi, M., & Totaro, M. W. (2025) · IEEE International Conference on Computing and Machine Intelligence (ICMI) · doi:10.1109/icmi65310.2025.11141106 · Cited by 4 · see citing works
Trinidad, A. H., Murillo-Ortiz, B. O., Guzmán-Cabrera, R., & Córdova–Fraga, T. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.113826 · Cited by 4 · see citing works
Ogundokun, R. O., Abikoye, O. C., Sahu, A. K., Akinrotimi, A. O., Babatunde, A. N., Sadiku, P. O., & Olabode, O. J. (2024) · Springer eBooks · doi:10.1007/978-981-99-9803-6_1 · Cited by 4 · see citing works
Chi, N., Wang, X., Yu, Y., Wu, M., & Yu, J. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/2203737 · Cited by 4 · see citing works
RetractedReview of Machine Learning and Deep Learning Techniques for Medical Image Analysis
Saratkar, S., Raut, R., Thute, T., Chaudhari, A., & Thakre, G. (2024) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici62503.2024.10696262 · Cited by 3 · see citing works
Attentive 3D‐Ghost Module for Dynamic Hand Gesture Recognition with Positive Knowledge Transfer
Li, J., Liu, R., Kong, D., Wang, S., Wang, L., Yin, B., & Gao, R. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/5044916 · Cited by 3 · see citing works
Breast Tumor Segmentation in Ultrasound Images Based on U-NET Model
Michael, E., Ma, H., & Qi, S. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-14054-9_3 · Cited by 3 · see citing works
Tyagi, L., Singh, D., & Goyal, N. (2024) · AIP conference proceedings · doi:10.1063/5.0234321 · Cited by 3 · see citing works
Special Issue: Artificial Intelligence in Advanced Medical Imaging
Jia, H., Jiao, Q., & Liu, M. (2024) · Bioengineering · doi:10.3390/bioengineering11121229 · Cited by 3 · see citing works
ELIME: Exact Local Interpretable Model-Agnostic Explanation
Qian, J., Du, X., Pan, R., Ling, M., & Ding, H. (2025) · The European Journal on Artificial Intelligence · doi:10.1177/30504554251325141 · Cited by 2 · see citing works
Ajabani, D. H. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0141202 · Cited by 2 · see citing works
A Review on Medical Image Segmentation Using Deep Learning
Sherifa, C. A., & Kanisha, B. (2024) · Algorithms for intelligent systems · doi:10.1007/978-981-97-5791-6_6 · Cited by 2 · see citing works
Improving Pneumonia Detection Using Segmentation and Image Enhancement
Thipakaran, E., Gandhiraj, R., & Panda, M. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-9379-4_58 · Cited by 2 · see citing works
New Advances in Magnetic Resonance Imaging
Larrivee, D. C. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.111259 · Cited by 2 · see citing works
Dermoskopik görüntülerde lezyon bölütleme için iyileştirme önerileri
Arpacı, S. A., & Varlı, S. (2024) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gazimmfd.1335533 · Cited by 1 · see citing works
Interactively Assisting Glaucoma Diagnosis with an Expert Knowledge-Distilled Vision Transformer
Li, Z., Wei, H., Sun, K., Cui, L., Li, D., Feiner, S., & Thakoor, K. A. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719719 · Cited by 1 · see citing works
Wang, L., Feng, L., Zhang, J., Li, Y., Fan, Y., Liu, P., Chen, Q., Hong, Y., & Huang, F. L. (2025) · Scientific Reports · doi:10.1038/s41598-025-25004-5 · Cited by 1 · see citing works
Hadi, F., Bilal, O., & Asif, S. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.114698 · Cited by 1 · see citing works
Identification Methods for Structural Problems of Bridges Based on Deep Convolutional Neural Network
Liu, G., Cai, S., Han, W., Guo, H., Ni, C., & Ni, Z. (2024) · Sensors and Materials · doi:10.18494/sam4717 · Cited by 1 · see citing works
Luo, X., Zhang, J., Cai, G., Wu, Y., & Ma, K. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/2937056 · Cited by 1 · see citing works
RetractedPedestrian detection based on improved EfficientDetPreprint
Zhang, C., Yu, M., Min, J., Fu, M., & Zheng, B. (2023) · Research Square · doi:10.21203/rs.3.rs-3611172/v1 · Cited by 1 · see citing works
Muthuswamy, J., Reddy, C. V., & Narayanaswamy, R. (2023) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v13i4.pp4076-4086
Yoshitsugu, K., Kishimoto, K., & Takemura, T. (2024) · International Conference on Intelligent Medicine and Image Processing (IMIP) · doi:10.1145/3669828.3669831
Analyzing Medical Images through Advanced Deep Learning Models
Bhoite, A. S., Pati, P. R., Mishra, G., Lipi, K., Desai, J., & Rani, D. S. (2024) · International Conference on Healthcare Innovations, Software and Engineering Technologies (HISET) · doi:10.1109/hiset61796.2024.00065
Gaussian Noise in Medical Imaging Systems: Sources, Effects, and Techniques for Mitigation
Sharma, M., & Ganie, K. A. (2025) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2025.1409000064
Image Segmentation and Quantification Using Wavelet Transform
Asharani, R., & Murthy, M. V. C. (2026) · Studies in systems, decision and control · doi:10.1007/978-3-032-10016-0_43
3D Segment Anything Model With Visual Mamba for Diagnosing Placenta Accreta Spectrum
Zhang, Y., He, F., Peng, L., Yan, T., Zhang, P., Song, T., Du, L., & Chen, D. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3694185
Aderemi, E. T., Fagbola, T. M., Fagbuagun, O. A., Ogundokun, R. O., & Owolawi, P. A. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02273-8
- Authors
- Adegun, A. A., Viriri, S., & Ogundokun, R. O.
- Affiliations
- University of KwaZulu-Natal (ZA)
- Landmark University (NG)
- Corresponding author
- Viriri, Serestina
- Venue
- Computational Intelligence and Neuroscience
- Publisher
- Wiley
- Published
- 2021-01
- Volume
- 2021
- Issue
- 1
- ISSN
- 1687-5265 (ISSN-L)
- 1687-5273
- OpenAlex
- W3163615921
- Cited by
- 52
- References
- 23
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 15 · 2023: 13 · 2022: 11 · 2021: 3
- FWCI
- 3.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Computational Intelligence and Neuroscience (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- medical image segmentation, region of interest localization, skin lesion segmentation, retinal image segmentation, brain MRI segmentation, brain tumor detection, skin cancer diagnosis, eye disease diagnosis, diabetic retinopathy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
