Conference Paper2026Cited by 1

Ultrasound Image-Based Liver Disease Detection Using Transfer Learning: A Comparative Analysis of CNN and Their Variants

Fatin Israq, Sefatul Wasi, Sazin Bin Noor, Siam Tahsin Bhuiyan, Saadia Binte Alam, Rashedur Rahman

2026 7th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI)

IEEE, pp. 744-750

  • Good Health and Well-being — Relevance: 53%
DOI: 10.1109/icmcsi67283.2026.11412706URL

Abstract

Liver disease is a worldwide health concern, and accurate early detection is essential to prevent severe complications. Ultrasound imaging is frequently used for liver assessment because it is safe and affordable, but interpretation can be subjective and error prone. This study compares the performance of convolutional neural networks (CNNs)EfficientNet, MobileNet, and ConvNeXt variants in the publicly accessible annotated dataset of ultrasound images into benign, malignant, and normal categories. Results show that MobileNetV2 achieved the highest overall performance with an accuracy of 78%, recall of 78%, AUROC of 0.89 and inference time 28.47 MS, while EfficientNetB2 also displayed a strong balance between all metrics with an accuracy of 77% and inference time$\mathbf{7 4. 6 8}$MS. In contrast, ConvNeXt models offered competitive but not superior performance, managing an average accuracy of 74% and high inference time. These results suggest that lightweight and moderately scaled CNNs, which strike a balance between accuracy and computational efficiency, show promise for liver disease detection from ultrasound images, with MobileNetV2 benefiting from a reduced parameter count and energy-efficient design that enables faster inference and lower computational cost.

Keywords

  • ConvNeXt
  • liver disease detection
  • MobileNetV2
  • transfer learning
  • convolutional neural networks
  • EfficientNet-B2
  • ultrasonography
  • inference latency
  • lightweight convolutional neural network

CCDS Authors

Siam Tahsin Bhuiyan
Siam Tahsin Bhuiyan

Siam Tahsin Bhuiyan

  • Intern, CCDS

Bachelors

Independent University, Bangladesh

Published at

Scientific Reports

Saadia Binte Alam, PhD
Saadia Binte Alam, PhD

Saadia Binte Alam, PhD

  • Director, MIRA & WiSE Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Md. Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md. Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

References

  1. 1.Jie Hu, Li Shen, Gang Sun. (2018). Squeeze-and-Excitation Networks. , 7132–7141[10.1109/cvpr.2018.00745]
  2. 2.Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. , 4510–4520[10.1109/cvpr.2018.00474]
  3. 3.Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. (2022). A ConvNet for the 2020s. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11966–11976[10.1109/cvpr52688.2022.01167]
  4. 4.Alejandro Forner, María Reig, Jordi Bruix. (2018). Hepatocellular carcinoma. The Lancet, 391(10127), 1301–1314[10.1016/s0140-6736(18)30010-2]
  5. 5.Justin Ker, Lipo Wang, Jai Rao, Tchoyoson Lim. (2017). Deep Learning Applications in Medical Image Analysis. IEEE Access, 6, 9375–9389[10.1109/access.2017.2788044]
  6. 6.Hui Che, Lloyd G. Brown, David J. Foran, John L. Nosher, Ilker Hacihaliloglu. (2021). Liver disease classification from ultrasound using multi-scale CNN. International Journal of Computer Assisted Radiology and Surgery, 16(9), 1537–1548[10.1007/s11548-021-02414-0]
  7. 7.Se-Yeol Rhyou, Jae-Chern Yoo. (2021). Cascaded Deep Learning Neural Network for Automated Liver Steatosis Diagnosis Using Ultrasound Images. Sensors, 21(16), 5304[10.3390/s21165304]
  8. 8.Taewoo Kim, Dong Hyun Lee, Eun-Kee Park, Sanghun Choi. (2021). Deep Learning Techniques for Fatty Liver Using Multi-View Ultrasound Images Scanned by Different Scanners: Development and Validation Study. JMIR Medical Informatics, 9(11), e30066[10.2196/30066]
  9. 9.D Santhosh Reddy, R Bharath, P Rajalakshmi. (2018). A Novel Computer-Aided Diagnosis Framework Using Deep Learning for Classification of Fatty Liver Disease in Ultrasound Imaging. , 1–5[10.1109/healthcom.2018.8531118]
  10. 10.Jiaxiang Wu, Pan Zeng, Peizhong Liu, Guorong Lv. (2021). Automatic classification method of liver ultrasound standard plane images using pre-trained convolutional neural network. Connection Science, 34(1), 975–989[10.1080/09540091.2021.2015748]
  11. 11.Liang Zhang, Yueqing Huang, Min Huang, Chun-Hua Zhao, Yan-Jun Zhang, Yi Wang. (2024). Development of Cost-Effective Fatty Liver Disease Prediction Models in a Chinese Population: Statistical and Machine Learning Approaches. JMIR Formative Research, 8, e53654[10.2196/53654]
  12. 12.Gautham Paul, Ramkumar Govindaraj. (2023). Experimental Analysis for Non-Alcoholic Fatty Liver Disease in Ultrasound Images Based on Efficient Net Classifier. , 1–7[10.1109/icses60034.2023.10465508]
  13. 13.Fahad M. Alshagathrh, Saleh Musleh, Mahmood Alzubaidi, Jens Schneider, Mowafa S. Househ. (2023). Efficient Detection of Hepatic Steatosis in Ultrasound Images Using Convolutional Neural Networks: A Comparative Study. Traitement du signal, 40(5), 1781–1794[10.18280/ts.400501]

Cited By

  1. 1.Sayeda Subha Ashrafi, Tushar Ahmed Shishir. (2026). Deep Learning-Based Automated Breast Cancer Detection from Histopathology Images: Benchmarking CNN and Transformer Architectures. , 1–6[10.1109/peccii70991.2026.11662018]
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