Related Experiment Video
Updated: Jul 17, 2026
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
Published on: November 19, 2018
Using a deep learning neural network for the identification of malignant cells in effusion cytology material
Parikshit Sanyal1, Pranab Dey2
1Department of Laboratory Medicine, Command Hospital Chandimandir, Chandimandir, India.
Aim:
To evaluate the application of an artificial neural network in the detection of malignant cells in effusion samples.
Materials And Methods:
In this retrospective study, we selected 90 cases of effusion cytology samples over 2 years. There were 52 cases of metastatic adenocarcinoma and 38 benign effusion samples. In each case, an average of five microphotographs from the representative areas were taken at 40× magnification from Papanicolaou-stained samples. A total of 492 images were obtained from these 90 cases. We applied a deep convolutional neural network (DCNN) model to identify malignant cells in the cytology images of effusion cytology smears. The training was performed for 15 epochs. The model consisted of 783 layers with 188 convolution-max pool layers in between.
Results:
In the test set, the DCNN model correctly identified 54 of 56 images of benign samples and 49 out of 56 images of malignant samples. It showed 88% sensitivity, 96% specificity and 96% positive predictive value in the screening of malignant cases in effusion. The area under the receiver operating curve was 0.92.
Conclusion:
DCNN is a unique technology that can detect malignant cells from cytological images. The model works rapidly and there is no bias in cell selection or feature extraction. The present DCNN model is promising and can have a significant impact on the diagnosis of malignancy in cytology.
