Research on Image Classification of Pathology Based on Deep Learning

DOI: https://doi.org/10.62517/jike.202404111

Author(s)

Runcheng Wuzhao1, Gao Gong2, Shi Cao2, Chaomin Chen2, Wei Wang2,*

Affiliation(s)

1Southern Medical University, Guangzhou, Guangdong, China 2Tumor Hospital affiliated to Guangzhou Medical University, Guangzhou, Guangdong, China *Corresponding Author.

Abstract

Histopathological image classification based on in-depth study is one of the significant methods for diagnosing diseases. Pathology image information is complex, diverse, and has a large number of features, which leads to difficult classification and low diagnostic accuracy. Building computer-aided diagnostic tools using image processing and artificial intelligence techniques can dramatically improve pathologists’ productivity and reduce error rates. The classification methods for histopathological images mainly included the traditional machine learning methods based on manual feature extraction and deep learning-based pathology image classification methods. The classification accuracy of principal component analysis (PCA), random forest (RF), and support vector machine (SVM) in the traditional machine learning methods were 99.05%, 88%, and 80-85%, respectively. In the deep learning method, the classification accuracy of the convolutional neural network GoogleNet, AlexNet, and deep residual network ResNet is 91%, 99.74%, and 97.4%, respectively. Compared with the shortcomings of traditional classification methods of pathological images, the histopathology image classification methods of deep learning have obvious advantages, but there is also over-dependence on data labeling, feature extraction, and algorithm models, which need to be improved and improved in the future.

Keywords

Disease Diagnosis; Histopathological Images; Deep Learning; Image Classification

References

[1]Gao MH, Shang XQ. Using artificial intelligence to predict cancer susceptibility and survival. Advances in Biochemistry and Biophysics, 2022, 49(9): 1687- 1702. [2]Veta M, Pluim J PW, Van Diest P J, et al. Breast Cancer Histopathology Image Analysis: A Review. IEEE Transactions on Biomedical Engineering, 2014, 61(5): 1400- 1411. [3]Liu QR, Zhao Y. Application of artificial intelligence technology in the diagnosis and treatment of laryngeal cancer. Chinese Journal of Otolaryngology and Cranial Base Surgery, 2021, 27(5): 530-533. [4]Naik, Shivang, Scott Doyle, Shannon C. Agner, Anant Madabhushi, Michael D. Feldman and John E. Tomaszeweski. “Automated gland and nuclei segmentation for grading of prostate and breast cancer histopathology.” 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (2008): 284-287. [5]Ren C, Hao S, Wang F, et al. Dynamic contrast optical coherence tomography (DyC-OCT) for label-free live cell imaging. Commun Biol. 2024; 7(1):278. [6]Baidar Bakht A, Javed S, Gilani SQ, Karki H, Muneeb M, Werghi N. DeepBLS: Deep Feature-Based Broad Learning System for Tissue Phenotyping in Colorectal Cancer WSIs. J Digit Imaging. 2023; 36(4):1653-1662. [7]Wang P, Hu X, Li Y, et al. Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Signal Processing, 2016, 122(MAY):1- 13. [8]Zhang Y, Zhang B, Coenen F, et al. One-class kernel subspace ensemble for medical image classification. EURASIP Journal on Advances in Signal Processing, 2014, 2014(1):1- 13. [9]Spanhol FA, Oliveira LS, Petitjean C, Heutte L. A Dataset for Breast Cancer Histopathological Image Classification. IEEE Trans Biomed Eng. 2016; 63(7):1455-1462. [10]Qi X, Xing F, Foran D J , et al. Robust Segmentation of Overlapping Cells in Histopathology Specimens Using Parallel Seed Detection and Repulsive Level Set. IEEE transactions on bio-medical engineering, 2012, 59(3):754. [11]Zeiler, M. D., & Fergus, R. (2014). Visualizing and understanding convolutional networks. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part I 13 (pp. 818-833). Springer International Publishing. [12]Wang H, Li X, Liu XF, et al. Classification of breast cancer histopathologicalimages based on ResNet50 network. Journal of China University of Metrology, 2019, 30(001):P.72-77. [13]Jiang Y, Chen L, Zhang H, et al. Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PloS one, 2019, 14(3): e0214587. [14]Zhong Z, Zheng M, Mai H, et al. Cancer image classification based on DenseNet model//Journal of Physics: Conference Series. IOP Publishing, 2020, 1651(1): 012143. [15]Chen C M, Huang Y S, Fang PW, et al. A computer‐aided diagnosis system for differentiation and delineation of malignant regions on whole‐slide prostate histopathology image using spatial statistics and multidimensional DenseNet. Medical physics, 2020, 47(3): 1021- 1033. [16]Li X, Radulovic M, Kanjer K, et al. Discriminative pattern mining for breast cancer histopathology image classification via fully convolutional autoencoder. IEEE Access, 2019, 7: 36433-36445.