Sage Journals: Discover world-class research

Abstract

Cancer represents a severe hazard to human well-being, and the precise segmentation of cancer cells plays a pivotal role in cancer diagnosis and therapy. In order to enhance the accuracy and efficiency of cancer cell segmentation, this study proposes the EB0-SEGP (EfficientNet-B0 SE Block Gaussian Pyramid) model constructed based on EfficientNet-B0 and the Gaussian pyramid. The model combines the channel attention mechanism (SE Block), up-sampling layers, and NIN blocks, and is optimized on the foundation of the traditional U-Net framework. During the research process, cancer cell smear images were collected from the open-source website CSDN, and after pre-processing and data augmentation, Dataset A was constructed. Accuracy, loss value, and Dice coefficient were used as indicators to evaluate the performance of the model. The research results show that the EB0-SEGP model achieves an accuracy of 88%, a Dice coefficient of 0.88, and a loss value of 0.06 on the test set, demonstrating a notable improvement compared to U-Net and ERU-Net. Meanwhile, the standard deviation of the training curve is significantly lower than that of the comparison models, indicating a lighter degree of overfitting. This study provides a new technical means for cancer treatment and has important theoretical significance and clinical application value in the field of cancer diagnosis and treatment.

Keywords

cancer cell smear segmentation EfficientNet-B0 Gaussian pyramid

Get full access to this article

View all access options for this article.

References

Zheng

Zhou

, et al. Mc-unet: Multi-scale convolution unet for bladder cancer cell segmentation in phase-contrast microscopy images. In: 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019, pp. 1197–1199.

Sikder

Das

Anwar

AMS

. Cancer cell segmentation based on unsupervised clustering and deep learning. In: Intelligent Computing and Optimization: Proceedings of the 3rd International Conference on Intelligent Computing and Optimization 2020 (ICO 2020). Springer International Publishing, 2021, pp. 607–620.

Shah

Kang

. An optimized multi-organ cancer cells segmentation for histopathological images based on CBAM-residual U-Net. IEEE Access 2023; 11: 111608–111621.

Hiremath

. Breast cancer detection and classification using EfficientNet B0 and EfficientNet B0-HSV. National College of Ireland, 2022.

Behera

Das

Sethy

. Deep fine-KNN classification of ovarian cancer subtypes using EfficientNet-B0 extracted features: a comprehensive analysis. J Cancer Res Clin Oncol 2024; 150(7): 361.

Abd El-Ghany

Mahmood

Abd El-Aziz

. Adaptive dynamic learning rate optimization technique for colorectal cancer diagnosis based on histopathological image using EfficientNet-B0 deep learning model. Electronics 2024; 13(16): 3126.

Jia

Zhou

, et al. Rediscovering bce loss for uniform classification. arxiv preprint arxiv:2403.07289, 2024.

Gao

Huang

, et al. Adaptive spatial-bce loss for weakly supervised semantic segmentation. In: European Conference on Computer Vision. Springer Nature Switzerland, 2022, pp. 199–216.

Liu

Zhang

, et al. DV-Net: accurate liver vessel segmentation via dense connection model with D-BCE loss function. Knowl Base Syst 2021; 232: 107471.

10.

Zhao

Qian

Zhang

, et al. Rethinking dice loss for medical image segmentation. In: 2020 IEEE international conference on data mining (ICDM). IEEE, 2020, pp. 851–860.

11.

Sun

Meng

, et al. Dice loss for data-imbalanced NLP tasks. arxiv preprint arxiv:1911.02855, 2019.

12.

Yeung

Rundo

Nan

, et al. Calibrating the dice loss to handle neural network overconfidence for biomedical image segmentation. J Digit Imag 2023; 36(2): 739–752.

13.

Ullrich

Meeds

Welling

. Soft weight-sharing for neural network compression. arxiv preprint arxiv:1702.04008, 2017.

14.

Nowlan

Hinton

. Simplifying neural networks by soft weight sharing. In: The mathematics of generalization. CRC Press, 2018, pp. 373–394.

15.

Xie

Chen

, et al. Weight-sharing neural architecture search: a battle to shrink the optimization gap. ACM Comput Surv 2021; 54(9): 1–37.

16.

Siddique

Paheding

Elkin

, et al. U-net and its variants for medical image segmentation: a review of theory and applications. IEEE Access 2021; 9: 82031–82057.

17.

Cao

Liang

, et al. Medical image segmentation based on U-net: a review. J Imag Sci Technol 2020; 64(2): 1–12.

18.

Yin

Sun

, et al. [Retracted] U‐Net‐based medical image segmentation. J Healthc Eng 2022; 2022(1): 4189781.

Cancer cell smear image segmentation model based on EfficientNet-B0 collaborating with Gaussian pyramid

Abstract

Keywords

Get full access to this article

References