Automatic classification of cervical cancer from cytological images by using convolutional neural network

Abstract

Cervical cancer (CC) is one of the most common gynecologic malignancies in the world. The incidence and mortality keep high in some remote and poor medical condition regions in China. In order to improve the current situation and promote the pathologists’ diagnostic accuracy of CC in such regions, we tried to propose an intelligent and efficient classification model for CC based on convolutional neural network (CNN) with relatively simple architecture compared with others. The model was trained and tested by two groups of image datasets, respectively, which were original image group with a volume of 3012 datasets and augmented image group with a volume of 108432 datasets. Each group has a number of fixed-size RGB images (227*227) of keratinizing squamous, non-keratinizing squamous, and basaloid squamous. The method of three-folder cross-validation was applied to the model. And the classification accuracy of the models, overall, 93.33% for original image group and 89.48% for augmented image group. The improvement of 3.85% has been achieved by using augmented images as input data for the model. The results got from paired-samples ttest indicated that two models’ classification accuracy has a significant difference (P<0.05). The developed scheme we proposed was useful for classifying CCs from cytological images and the model can be served as a pathologist assistance to improve the doctor’s diagnostic level of CC, which has a great meaning and huge potential application in poor medical condition areas in China.

Emerging role of deep learning‐based artificial intelligence in tumor pathology

“…[33] developed CNN algorithms to sort each image tile into individual growth pattern and generate a probability map for a WSI, facilitating pathologists to quantitatively report the major and more malignant components of lung adenocarcinoma, such as micropapillary and solid components. Similarly, DL‐based AL were performed for multi‐categorization of colorectal polyp [34], ovarian cancer [35], thyroid tumor [36], breast tumor [37], and cervical squamous cell carcinoma [38]. On the basis of cytological image, AI could recognize the histological subtypes of lung cancer with an accuracy of 60%‐89% [39].…”

“…Current AI algorithms are mainly established on small‐scale data and images from single‐center. The data from single center were still deviation, although researchers have developed methods to augment the dataset, including but not limited to random rotation and flipping, color jittering, and Gaussian blur [32, 35, 38, 39]. Variations exist in slide preparation, scanner models and digitization among different centers.…”

MicroRNA‐124: An emerging therapeutic target in cancer

“…Cervical cancer has become the most common gynecologic malignancy due to widespread human papillomavirus (HPV) infection. 96 Abnormal miR-124 expression due to promoter methylation is functionally involved in cervical cancer. The miR-124 promoter region containing CpG islands in normal cells is usually hypomethylated or unmethylated.…”

A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

“…In addition, this review discussed the deep ANNs method not only can be applied in the field of breast histopathological image analysis, but also in the field of other closed microscopic image analysis, such as: Cervical histopathological analysis [167], [168], [169], cervical cytopathological analysis [170], [171], [172], stem cell analysis [173], [174], microbiological image analysis [175], [176], [177], sperm quality analysis [178], [179], [178], web-based platform for computer assisted diagnosis [180], [181], and rock microstructural analysis [182], [183]. No matter from the aspects of image pre-processing, feature extraction and selection, segmentation, and classification, or from the aspects of deep ANN model design and proposed framework idea, the methods of deep ANN summarized in this review can bring a new perspective to the research in other fields.…”