Skip to content
2000
Volume 19, Issue 1
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566

Abstract

Introduction

Nowadays, Artificial intelligence and machine learning have emerged as a powerful tool for the analysis of medical images such as MRI scans. This technology holds significant potential to improve diagnostic services and accelerate medical advances by facilitating clinical decision-making.

Methods

In this work, we developed a Convolutional Neural Network (CNN) model specifically designed for the classification of medical images. Using a selected database, the model achieved a classification accuracy of 92%. To further improve the performance, we leveraged the pre-trained VGG16 model, which increased the classification accuracy to 100%. Additionally, we preprocessed the MRI images using the Roboflow platform and then developed YOLOv5 models for the detection of tumors, infections, and cancerous lesions.

Results

The results demonstrate a localization accuracy of 50.41% for these medical conditions.

Conclusion

This research highlights the value of AI-driven approaches in enhancing medical image analysis and their potential to support more accurate diagnoses and accelerate advancements in healthcare.

© 2026 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/rascs/10.2174/0126662558327739240925073925
2024-10-10
2025-12-13

  • From This Site

    /content/journals/rascs/10.2174/0126662558327739240925073925
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. DurandM. ShaikhA. BilliM. LechevallierE. “Medical applications of artificial intelligence: Opportunities & challenges”, Prog. En Urol.-.FMC2020302F63F68
     [Google Scholar]
  2. SonaliS. ViswanadhM.K. SinghR.P. AgrawalP. MehataA.K. PawdeD.M. R.Narendra Sonkar  MuthuM. S. Nanotheranostics: Emerging strategies for early diagnosis and therapy of brain cancer,.Nanotheranostics201821708610.7150/ntno.21638
     [Google Scholar]
  3. HuZ. TangJ. WangZ. ZhangK. ZhangL. SunQ. Deep learning for image-based cancer detection and diagnosis − A survey.Pattern Recognit.20188313414910.1016/j.patcog.2018.05.014
     [Google Scholar]
  4. BarkaN. AouniN.E. Deep learning for COVID-19 detectionPhD Thesis, Université de Ouargla, Ourgla, Algeria,2022 https://dspace.univ-ouargla.dz/jspui/handle/123456789/31388
    [Google Scholar]
  5. GuanQ. WangY. PingB. LiD. DuJ. QinY. LuH. WanX. XiangJ. Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: A pilot study.J. Cancer201910204876488210.7150/jca.28769 31598159
     [Google Scholar]
  6. ArunachalamS. SethumathavanG. An effective tumor detection in MR brain images based on deep CNN approach: I- YOLOV5.Appl. Artif. Intell.2022361215118010.1080/08839514.2022.2151180
     [Google Scholar]
  7. BochkovskiyA. WangC-Y. LiaoH-Y.M. YOLOv4: Optimal speed and accuracy of object detectionarXiv2020
     [Google Scholar]
  8. YounisA. QiangL. NyategaC.O. AdamuM.J. KawuwaH.B. Brain tumor analysis using deep learning and VGG-16 ensembling learning approaches.Appl. Sci.20221214728210.3390/app12147282
     [Google Scholar]
  9. NathN.D. BehzadanA.H. PaalS.G. Deep learning for site safety: Real-time detection of personal protective equipment.Autom. Construct.202011210308510.1016/j.autcon.2020.103085
     [Google Scholar]
  10. KrizhevskyA. SutskeverI. HintonG.E. ImageNet classification with deep convolutional neural networks, Proc. Advances in Neural Information Processing Systems(NIPS)25201210971105 https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
    [Google Scholar]
  11. ZhangZ. Non-invasive glioma grading using transfer learning from multiparametric MRI.Neuroradiology202264343344234665270
     [Google Scholar]
  12. LiX. Automated lung nodule detection and segmentation using deep learning.Radiology202329616674 36842787
     [Google Scholar]
  13. HuangJ. Prostate tumor segmentation with a hierarchical attention-guided network.Med. Image Anal.202277102361
     [Google Scholar]
  14. WangS. Predicting EGFR mutation status in lung adenocarcinoma on CT scans using deep learning.Radiology2021298227328010.1148/radiol.2019182391 30835184
     [Google Scholar]
  15. ChenY. Deep learning-based risk stratification for predicting recurrence in thyroid cancer.Endocrine2022762349357 35107758
     [Google Scholar]
  16. LuoY. ZhangY. SunX. DaiH. ChenX. Intelligent solutions in chest abnormality detection based on YOLOv5 and ResNet50.J. Healthc. Eng.20212021226763510.1155/2021/2267635
     [Google Scholar]
  17. HijabA. RushdiM.A. GomaaM.M. EldeibA. Breast cancer classification in ultrasound images using transfer learningFifth International Conference on Advances in Biomedical Engineering (ICABME)Tripoli, Lebanon201914
     [Google Scholar]
  18. FanY. LiY. ShiY. WangS. Application of YOLOv5 neural network based on improved attention mechanism in recognition of Thangka image defects.KSII Trans. Internet Inf. Syst. TIIS2022161245265
     [Google Scholar]
  19. ZhouZ. GouF. TanY. WuJ. A cascaded multi-stage framework for automatic detection and segmentation of pulmonary nodules in developing countries.IEEE J. Biomed. Health Inform.2022261156195630
     [Google Scholar]
  20. PandeyS.K. BhandariA.K. Morphological transfer learning based brain tumor detection using YOLOv5.Multimedia Tools Appl.20238316493434936610.1007/s11042‑023‑17367‑6
     [Google Scholar]
  21. AlomM.Z. HasanM. YakopcicC. TahaT.M. AsariV.K. Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image SegmentationarXiv preprint arXiv:1802.069552018
     [Google Scholar]
  22. RehmanA. NazS. RazzakM.I. AkramF. ImranM. A deep learning-based framework for automatic brain tumor classification using transfer learning.Circuits and Systems Signal Processing.202039757775
     [Google Scholar]
  23. AminJ. SharifM. YasminM. FernandesS.L. A distinctive approach in brain tumor detection and classification using MRI.Pattern Recognit. Lett.202013911812710.1007/s11042‑023‑17367‑6
     [Google Scholar]
/content/journals/rascs/10.2174/0126662558327739240925073925
Loading
Analysis and Classification of Medical Images Using Deep Learning Algorithms
Recent Advances in Computer Science and Communications 19, E26662558327739 (2026); https://doi.org/10.2174/0126662558327739240925073925
/content/journals/rascs/10.2174/0126662558327739240925073925
/content/journals/rascs/10.2174/0126662558327739240925073925
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Artificial intelligence; CNN; deep learning; medical image; VGG16; YOLOv5

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test