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Cited by 38Medical Image Segmentation TechniquesAI in cancer detectionAdvanced Neural Network Applications

RETRACTED: Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks

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Amin, Javaria, Anjum, Muhammad, Sharif, Muhammad, Kadry, Seifedine, Nadeem, Ahmed, Ahmad, Sheikh·2022·Diagnostics
DOI 10.3390/diagnostics12040823PMC PMC9025116
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Abstract

Worldwide, more than 1.5 million deaths are occur due to liver cancer every year. The use of computed tomography (CT) for early detection of liver cancer could save millions of lives per year. There is also an urgent need for a computerized method to interpret, detect and analyze CT scans reliably, easily, and correctly. However, precise segmentation of minute tumors is a difficult task because of variation in the shape, intensity, size, low contrast of the tumor, and the adjacent tissues of the liver. To address these concerns, a model comprised of three parts: synthetic image generation, localization, and segmentation, is proposed. An optimized generative adversarial network (GAN) is utilized for generation of synthetic images. The generated images are localized by using the improved localization model, in which deep features are extracted from pre-trained Resnet-50 models and fed into a YOLOv3 detector as an input. The proposed modified model localizes and classifies the minute liver tumor with 0.99 mean average precision (mAp). The third part is segmentation, in which pre-trained Inceptionresnetv2 employed as a base-Network of Deeplabv3 and subsequently is trained on fine-tuned parameters with annotated ground masks. The experiments reflect that the proposed approach has achieved greater than 95% accuracy in the testing phase and it is proven that, in comparison to the recently published work in this domain, this research has localized and segmented the liver and minute liver tumor with more accuracy.

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  • In one notable application, liver tumor 3D scans were encrypted and reconstructed with a PSNR of 30.42 and SSIM of 0.94, ensuring that sensitive medical information remains secure and accurate during remote consultations or telemedicine applications.

    “In one notable application, liver tumor 3D scans were encrypted and reconstructed with a PSNR of 30.42 and SSIM of 0.94, ensuring that sensitive medical information remains secure and accurate during remote consultations or telemedicine applications [65].”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [65]

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Cited by

Sorted by citations

Authors
Amin, J., Anjum, M., Sharif, M., Kadry, S., Nadeem, A., & Ahmad, S.
Affiliations
  • University of Wah (PK)
  • National University of Technology (PK)
  • COMSATS University Islamabad (PK)
  • Noroff University College (NO)
  • King Saud University (SA)
Corresponding author
Kadry, Seifedine
ORCID
Venue
Diagnostics
Publisher
MDPI AG
Published
2022-03-27
Volume
12
Issue
4
Pages
823
ISSN
2075-4418 (ISSN-L)
DOI
10.3390/diagnostics12040823
PMC
PMC9025116
OpenAlex
W4220836030
Cited by
38
References
6
Citations by year
2026: 1 · 2025: 12 · 2024: 10 · 2023: 10 · 2022: 5
FWCI
3.03
Type
journal article
Language
en
Status
Retracted
Update notices
retraction (2026-08-26)
Open access
Licence
Locations
  • Diagnostics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • 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 › Artificial Intelligence › AI in cancer detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
liver tumor segmentation, liver cancer detection, computed tomography, YOLOv3, 3D semantic segmentation, synthetic image generation, generative adversarial networks, transfer learning, ResNet50, InceptionResNetV2, DeepLabV3+, retracted article

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