Skip to main content
SpringerLink
Log in

Integrating CNN along with FAST descriptor for accurate retrieval of medical images with reduced error probability

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The size of medical image repositories is continuously growing due to the widespread use of digital imaging data in hospitals. This overlays the way for more medical records to be stored in the future. An effective image retrieval system must be designed to make retrieving medical images from datasets as simple as possible. Using diverse feature extraction procedures, a number of researchers have created several picture retrieval frameworks. However, a semantic gap caused by poor extraction of low and high-level features is a fundamental problem in traditional image retrieval frameworks. As a result, during the construction of a retrieval framework, an effective feature extraction technique must be included for proper extraction of both level characteristics. The present research aims at designing modified Convolutional Neural Network(CNN) for the effective retrieval of medical images. The proposed process is performed using two models such as training and testing model. In the training phase, the features are learned using Features from Accelerated Segment Test with CNN (FAST-CNN) and stored in the database. Subsequently, in the testing process, a query image is retrieved from the dataset based on the feature matching process using Minkowski distance. The performance of the proposed retrieval framework is tested on three medical datasets using some of the metrics such as accuracy, sensitivity, precision, and F1 score. Using the proposed retrieval framework, accurate retrieval with a lesser error rate is achieved and the accuracy reached using this proposed retrieval framework is around 94%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Data availability

There is no availability of data or materials available or report for the manuscript.

Code availability

No code is available for this manuscript.

References

  1. Ahmed TI, Bhola J, Shabaz M, Singla J, Rakhra M, More S, Samori IA (2022) Fuzzy logic-based systems for the diagnosis of chronic kidney disease. Biomed Res Int 2022:2653665

    Google Scholar 

  2. Cai Y, Li Y, Qiu C, Ma J, Gao X (2019) Medical image retrieval based on convolutional neural network and supervised hashing. IEEE Access 7:51877–51885

    Article  Google Scholar 

  3. Chen J, Ying H, Liu X, Gu J, Feng R, Chen T … Wu J (2020) A transfer learning based super-resolution microscopy for biopsy slice images: the joint methods perspective. IEEE/ACM Trans Comput Biol Bioinform 18(1):103–113

  4. Chen T, Liu X, Feng R, Wang W, Yuan C, Lu W … Wu J (2021) Discriminative cervical lesion detection in colposcopic images with global class activation and local bin excitation. IEEE J Biomed Health Inform

  5. Dhingra S, Bansal P (2021) Relative examination of texture feature extraction techniques in image retrieval systems by employing neural network: an experimental review. In: Proceedings of international conference on artificial intelligence and applications. Springer, Singapore, pp 337–349

  6. Dubey RS, Roy SK, Chakraborty S, Mukherjee S, Baran Chaudhuri B (2019) Local bit-plane decoded convolutional neural network features for biomedical image retrieval. Neural Comput Appl:1–13

  7. Feng R, Liu X, Chen J, Chen DZ, Gao H, Wu J (2020) A deep learning approach for colonoscopy pathology WSI analysis: accurate segmentation and classification. IEEE J Biomedical Health Inf 25(10):3700–3708

    Article  Google Scholar 

  8. Haque IRI, Neubert J (2020) Deep learning approaches to biomedical image segmentation. Inf Med Unlocked 18:100297

    Article  Google Scholar 

  9. Haripriya P, Porkodi R (2021) Parallel deep convolutional neural network for content based medical image retrieval. J Ambient Intell Humaniz Comput 12:781–795

    Article  Google Scholar 

  10. Hassan G, Hosny KM, Farouk RM, Alzohairy AM (2020) An efficient retrieval system for biomedical images based on radial associated laguerre moments. IEEE Access 8:175669–175687

  11. Hassan G, Hosny KM, Farouk RM, Alzohairy AM (2020) Efficient quaternion moments for representation and retrieval of biomedical color images. Biomed Eng: Appl Basis Commun 32(05):2050039

  12. Khalid H, Hussain M, Al Ghamdi MA, Khalid T, Khalid K, Khan MA … Ahmed A (2020) A comparative systematic literature review on knee bone reports from mri, x-rays and ct scans using deep learning and machine learning methodologies. Diagnostics 10(8):518

  13. Khwildi R, Zaid AO, Dufaux F (2021) Query-by-example HDR image retrieval based on CNN. Multimed Tools Appl:1–16

  14. Kumar Y, Aggarwal A, Tiwari S, Singh K (2018) An efficient and robust approach for biomedical image retrieval using Zernike moments. Biomed Signal Process Control 39:459–473

    Article  Google Scholar 

  15. Li X, Yang J, Ma J (2020) Large scale category-structured image retrieval for object identification through supervised learning of CNN and SURF-based matching. IEEE Access 8:57796–57809

    Article  Google Scholar 

  16. Li X, Yang J, Ma J (2021) Recent developments of content-based image retrieval (CBIR). Neurocomputing 452:675–689

    Article  Google Scholar 

  17. Liu C-Y, Way CC-C, Tai W-K (2019) Image resizing using fuzzy inferences. IET Image Proc 13(12):2058–2066

    Article  Google Scholar 

  18. Lu H, Zhang M, Xu X, Li Y, Shen HT (2020) Deep fuzzy hashing network for efficient image retrieval. IEEE Trans Fuzzy Syst 29(1):166–176

    Article  Google Scholar 

  19. Montazeran M, Caramella D, Fatehi M (2021) Patient safety in radiology. In: Textbook of patient safety and clinical risk management. Springer, Cham, pp 309–318

  20. Owais M, Arsalan M, Choi J, Park KR (2019) Effective diagnosis and treatment through content-based medical image retrieval (CBMIR) by using artificial intelligence. J Clin Med 8(4):462

    Article  Google Scholar 

  21. Owais M, Arsalan M, Mahmood T, Kang JK, Park KR (2020) Automated diagnosis of various gastrointestinal lesions using a deep learning–based classification and retrieval framework with a large endoscopic database: model development and validation. J Med Int Res 22(11):e18563

  22. Öztürk Ş (2020) Stacked auto-encoder based tagging with deep features for content-based medical image retrieval. Expert Syst Appl 161:113693

    Article  Google Scholar 

  23. Qu Z, Mei J, Liu L, Zhou D-Y (2020) Crack detection of concrete pavement with cross-entropy loss function and improved VGG16 network model. IEEE Access 8:54564–54573

    Article  Google Scholar 

  24. Rashad M, Nooh S, Afifi I, Abdelfatah M (2022) Effective of modern techniques on content-based medical image retrieval: a survey

  25. Srivastava A, Aggarwal AK (2018) Medical image fusion in spatial and transform domain: a comparative analysis. In: Handbook of research on advanced concepts in real-time image and video processing. IGI Global, pp 281–300

  26. Sun Q, Zhang Y, Wang J, Gao W (2017) An improved FAST feature extraction based on RANSAC method of vision/SINS integrated navigation system in GNSS-denied environments. Adv Space Res 60(12):2660–2671

    Article  Google Scholar 

  27. Su Y, Zhong Y, Zhu Q, Zhao J (2021) Urban scene understanding based on semantic and socioeconomic features: From high-resolution remote sensing imagery to multi-source geographic datasets. ISPRS J Photogramm Remote Sens 179:50–65

    Article  Google Scholar 

  28. Thukral R, Arora AS, Kumar A (2022) Denoising of thermal images using deep neural network. In: Proceedings of international conference on recent trends in computing. Springer, Singapore, pp 827–833

  29. Tzelepi M, Tefas A (2018) Deep convolutional learning for content based image retrieval. Neurocomputing 275:2467–2478

    Article  Google Scholar 

  30. Willemink MJ, Koszek WA, Hardell C, Wu J, Fleischmann D, Harvey H … Lungren MP (2020) Preparing medical imaging data for machine learning. Radiology 295(1):4–15

  31. Xiao J, Xu H, Zhao W, Cheng C, Gao H (2021) A prior-mask-guided few-shot learning for skin lesion segmentation. Computing:1–23

  32. Xiao J, Xu H, Gao H, Bian M, Li Y (2021) A weakly supervised semantic segmentation network by aggregating seed cues: the multi-object proposal generation perspective. ACM Trans Multimed Comput Commun Appl 17(1s):1–19

    Article  Google Scholar 

  33. Yang X, Li S, Chen Z, Chanussot J, Jia X, Zhang B … Chen P (2021) An attention-fused network for semantic segmentation of very-high-resolution remote sensing imagery. ISPRS J Photogramm Remote Sens 177:238–262

  34. Yelmanov S, Romanyshyn Y (2018) Image contrast enhancement using a modified histogram equalization. In: 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP). IEEE, pp 568–573

  35. Zhang Z, Han D, Dezert J, Yang Y (2018) A new adaptive switching median filter for impulse noise reduction with pre-detection based on evidential reasoning. Sig Process 147:173–189

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science & Engineering, USICT, Guru Gobind Singh Indraprastha University, Dwarka, Delhi, 110078, India

    Aman Dureja

  2. Department of Information Technology, Bhagwan Parshuram Institute of Technology, New Delhi, Delhi, 110089, India

    Aman Dureja

  3. Computer Science & Engineering, Bhagwan Parshuram Institute of Technology, New Delhi, Delhi, 110089, India

    Payal Pahwa

Authors
  1. Aman Dureja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Payal Pahwa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aman Dureja.

Ethics declarations

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informal consent

Informed consent was obtained from all individual participants included in the study.

Consent to participate

I have read and I understand the provided information.

Consent to publish

This article does not contain any Image or video to get permission.

Conflict of interest

There is no conflict of Interest between the authors regarding the manuscript preparation and submission.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dureja, A., Pahwa, P. Integrating CNN along with FAST descriptor for accurate retrieval of medical images with reduced error probability. Multimed Tools Appl 82, 17659–17686 (2023). https://doi.org/10.1007/s11042-022-13991-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13991-w

Keywords

Search

Navigation