Automatic Classification of Ovarian Cancer Types from Cytological Images Using Deep Convolutional Neural Networks

Overview

Journal Biosci Rep

Specialty Cell Biology

Date 2018 Mar 25

PMID 29572387

Citations 25

Authors

Miao Wu

Chuanbo Yan

Huiqiang Liu

Qian Liu

Affiliations

Soon will be listed here.

Abstract

Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images.

Citing Articles

OVision A raspberry Pi powered portable low cost medical device framework for cancer diagnosis.

Mehta S Sci Rep. 2025; 15(1):7124.

PMID: 40021723 PMC: 11871010. DOI: 10.1038/s41598-025-91914-z.

Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis.

Hoppener D, Aswolinskiy W, Qian Z, Tellez D, Nierop P, Starmans M BJS Open. 2024; 8(6).

PMID: 39471410 PMC: 11523050. DOI: 10.1093/bjsopen/zrae127.

Preoperative Molecular Subtype Classification Prediction of Ovarian Cancer Based on Multi-Parametric Magnetic Resonance Imaging Multi-Sequence Feature Fusion Network.

Du Y, Wang T, Qu L, Li H, Guo Q, Wang H Bioengineering (Basel). 2024; 11(5).

PMID: 38790338 PMC: 11117786. DOI: 10.3390/bioengineering11050472.

A Review of Artificial Intelligence in Breast Imaging.

Al-Karawi D, Al-Zaidi S, Helael K, Obeidat N, Mouhsen A, Ajam T Tomography. 2024; 10(5):705-726.

PMID: 38787015 PMC: 11125819. DOI: 10.3390/tomography10050055.

Role of artificial intelligence in digital pathology for gynecological cancers.

Wang Y, Gao S, Xiao Q, Li C, Grzegorzek M, Zhang Y Comput Struct Biotechnol J. 2024; 24:205-212.

PMID: 38510535 PMC: 10951449. DOI: 10.1016/j.csbj.2024.03.007.

References

Miki Y, Muramatsu C, Hayashi T, Zhou X, Hara T, Katsumata A . Classification of teeth in cone-beam CT using deep convolutional neural network. Comput Biol Med. 2016; 80:24-29. DOI: 10.1016/j.compbiomed.2016.11.003. View

Chang C, Chen H, Chang Y, Yang M, Lo C, Ko W . Computer-aided diagnosis of liver tumors on computed tomography images. Comput Methods Programs Biomed. 2017; 145:45-51. DOI: 10.1016/j.cmpb.2017.04.008. View

Sharma H, Zerbe N, Klempert I, Hellwich O, Hufnagl P . Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput Med Imaging Graph. 2017; 61:2-13. DOI: 10.1016/j.compmedimag.2017.06.001. View

Alharbi A, Tchier F . Using a genetic-fuzzy algorithm as a computer aided diagnosis tool on Saudi Arabian breast cancer database. Math Biosci. 2017; 286:39-48. DOI: 10.1016/j.mbs.2017.02.002. View

Pang S, Yu Z, Orgun M . A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images. Comput Methods Programs Biomed. 2017; 140:283-293. DOI: 10.1016/j.cmpb.2016.12.019. View

de Carvalho Filho A, Silva A, Cardoso de Paiva A, Nunes R, Gattass M . Computer-Aided Diagnosis of Lung Nodules in Computed Tomography by Using Phylogenetic Diversity, Genetic Algorithm, and SVM. J Digit Imaging. 2017; 30(6):812-822. PMC: 5681471. DOI: 10.1007/s10278-017-9973-6. View

Wahab N, Khan A, Lee Y . Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection. Comput Biol Med. 2017; 85:86-97. DOI: 10.1016/j.compbiomed.2017.04.012. View

Yilmaz E, Kayikcioglu T, Kayipmaz S . Computer-aided diagnosis of periapical cyst and keratocystic odontogenic tumor on cone beam computed tomography. Comput Methods Programs Biomed. 2017; 146:91-100. DOI: 10.1016/j.cmpb.2017.05.012. View

Jing L, Wang T, Zhao M, Wang P . An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox. Sensors (Basel). 2017; 17(2). PMC: 5335931. DOI: 10.3390/s17020414. View

10.

Bron E, Smits M, Papma J, Steketee R, Meijboom R, De Groot M . Multiparametric computer-aided differential diagnosis of Alzheimer's disease and frontotemporal dementia using structural and advanced MRI. Eur Radiol. 2016; 27(8):3372-3382. PMC: 5491625. DOI: 10.1007/s00330-016-4691-x. View

11.

Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S . Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE Trans Med Imaging. 2016; 35(5):1207-1216. DOI: 10.1109/TMI.2016.2535865. View

12.

Shibusawa M, Nakayama R, Okanami Y, Kashikura Y, Imai N, Nakamura T . The usefulness of a computer-aided diagnosis scheme for improving the performance of clinicians to diagnose non-mass lesions on breast ultrasonographic images. J Med Ultrason (2001). 2016; 43(3):387-94. DOI: 10.1007/s10396-016-0718-9. View

13.

Nishio M, Nagashima C . Computer-aided Diagnosis for Lung Cancer: Usefulness of Nodule Heterogeneity. Acad Radiol. 2017; 24(3):328-336. DOI: 10.1016/j.acra.2016.11.007. View

14.

Xu Y, Jia Z, Wang L, Ai Y, Zhang F, Lai M . Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinformatics. 2017; 18(1):281. PMC: 5446756. DOI: 10.1186/s12859-017-1685-x. View

15.

Han X . MR-based synthetic CT generation using a deep convolutional neural network method. Med Phys. 2017; 44(4):1408-1419. DOI: 10.1002/mp.12155. View

16.

Tanaka Y, Okada S, Satoh T, Matsumoto K, Oki A, Saida T . Differentiation of epithelial ovarian cancer subtypes by use of imaging and clinical data: a detailed analysis. Cancer Imaging. 2016; 16:3. PMC: 4752792. DOI: 10.1186/s40644-016-0061-9. View

17.

Teramoto A, Tsukamoto T, Kiriyama Y, Fujita H . Automated Classification of Lung Cancer Types from Cytological Images Using Deep Convolutional Neural Networks. Biomed Res Int. 2017; 2017:4067832. PMC: 5572620. DOI: 10.1155/2017/4067832. View

18.

Masood A, Sheng B, Li P, Hou X, Wei X, Qin J . Computer-Assisted Decision Support System in Pulmonary Cancer detection and stage classification on CT images. J Biomed Inform. 2018; 79:117-128. DOI: 10.1016/j.jbi.2018.01.005. View

19.

Labidi-Galy S, Treilleux I, Goddard-Leon S, Combes J, Blay J, Ray-Coquard I . Plasmacytoid dendritic cells infiltrating ovarian cancer are associated with poor prognosis. Oncoimmunology. 2012; 1(3):380-382. PMC: 3382863. DOI: 10.4161/onci.18801. View

20.

Suzuki K . Overview of deep learning in medical imaging. Radiol Phys Technol. 2017; 10(3):257-273. DOI: 10.1007/s12194-017-0406-5. View