Cervical cell multi-classification algorithm using global context information and attention mechanism

Multi-scale window transformer for cervical cytopathology image recognition

Cervical cancer is a major global health issue, particularly in developing countries where access to healthcare is limited. Early detection of pre-cancerous lesions is crucial for successful treatment and reducing mortality rates. However, traditional screening and diagnostic processes require cytopathology doctors to manually interpret a huge number of cells, which is time-consuming, costly, and prone to human experiences. In this paper, we propose a Multi-scale Window Transformer (MWT) for cervical cytopathology image recognition. We design multi-scale window multi-head self-attention (MW-MSA) to simultaneously integrate cell features of different scales. Small window self-attention is used to extract local cell detail features, and large window self-attention aims to integrate features from smaller-scale window attention to achieve window-to-window information interaction. Our design enables long-range feature integration but avoids whole image self-attention (SA) in ViT or twice local window SA in Swin Transformer. We find convolutional feed-forward networks (CFFN) are more efficient than original MLP-based FFN for representing cytopathology images. Our overall model adopts a pyramid architecture. We establish two multi-center cervical cell classification datasets of two-category 192,123 images and four-category 174,138 images. Extensive experiments demonstrate that our MWT outperforms state-of-the-art general classification networks and specialized classifiers for cytopathology images in the internal and external test sets. The results on large-scale datasets prove the effectiveness and generalization of our proposed model. Our work provides a reliable cytopathology image recognition method and helps establish computer-aided screening for cervical cancer. Our code is available at https://github.com/nmyz669/MWT, and our web service tool can be accessed at https://huggingface.co/spaces/nmyz/MWTdemo.

An efficient Fusion-Purification Network for Cervical pap-smear image classification

BACKGROUND AND OBJECTIVES

In cervical cell diagnostics, autonomous screening technology constitutes the foundation of automated diagnostic systems. Currently, numerous deep learning-based classification techniques have been successfully implemented in the analysis of cervical cell images, yielding favorable outcomes. Nevertheless, efficient discrimination of cervical cells continues to be challenging due to large intra-class and small inter-class variations. The key to dealing with this problem is to capture localized informative differences from cervical cell images and to represent discriminative features efficiently. Existing methods neglect the importance of global morphological information, resulting in inadequate feature representation capability.

METHODS

To address this limitation, we propose a novel cervical cell classification model that focuses on purified fusion information. Specifically, we first integrate the detailed texture information and morphological structure features, named cervical pathology information fusion. Second, in order to enhance the discrimination of cervical cell features and address the data redundancy and bias inherent after fusion, we design a cervical purification bottleneck module. This model strikes a balance between leveraging purified features and facilitating high-efficiency discrimination. Furthermore, we intend to unveil a more intricate cervical cell dataset: Cervical Cytopathology Image Dataset (CCID).

RESULTS

Extensive experiments on two real-world datasets show that our proposed model outperforms state-of-the-art cervical cell classification models.

CONCLUSIONS

The results show that our method can well help pathologists to accurately evaluate cervical smears.

MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis

One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated cervical cancer diagnosis models. However, the majority of these models suffer from over-fitting, parameter tuning, and gradient vanishing problems. To overcome these problems, in this paper a metaheuristics-based lightweight deep learning network (MLNet) is proposed. Initially, the hyper-parameters tuning problem of convolutional neural network (CNN) is defined as a multi-objective problem. Particle swarm optimization (PSO) is used to optimally define the CNN architecture. Thereafter, Dynamically hybrid niching differential evolution (DHDE) is utilized to optimize the hyper-parameters of CNN layers. Each particle of PSO and solution of DHDE together represent the possible CNN configuration. F-score is used as a fitness function. The proposed MLNet is trained and validated on three benchmark cervical cancer datasets. On the Herlev dataset, MLNet outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.6254%, 1.5178%, 1.5780%, 1.7145%, and 1.4890%, respectively. Also, on the SIPaKMeD dataset, MLNet achieves better performance than the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 2.1250%, 2.2455%, 1.9074%, 1.9258%, and 1.8975%, respectively. Finally, on the Mendeley LBC dataset, MLNet achieves better performance than the competitive models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.4680%, 1.5845%, 1.3582%, 1.3926%, and 1.4125%, respectively.

Deep learning for computational cytology: A survey

Computational cytology is a critical, rapid-developing, yet challenging topic in medical image computing concerned with analyzing digitized cytology images by computer-aided technologies for cancer screening. Recently, an increasing number of deep learning (DL) approaches have made significant achievements in medical image analysis, leading to boosting publications of cytological studies. In this article, we survey more than 120 publications of DL-based cytology image analysis to investigate the advanced methods and comprehensive applications. We first introduce various deep learning schemes, including fully supervised, weakly supervised, unsupervised, and transfer learning. Then, we systematically summarize public datasets, evaluation metrics, versatile cytology image analysis applications including cell classification, slide-level cancer screening, nuclei or cell detection and segmentation. Finally, we discuss current challenges and potential research directions of computational cytology.

Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model

Recently, artificial intelligence (AI) with deep learning (DL) and machine learning (ML) has been extensively used to automate labor-intensive and time-consuming work and to help in prognosis and diagnosis. AI's role in biomedical and biological imaging is an emerging field of research and reveals future trends. Cervical cell (CCL) classification is crucial in screening cervical cancer (CC) at an earlier stage. Unlike the traditional classification method, which depends on hand-engineered or crafted features, convolution neural network (CNN) usually categorizes CCLs through learned features. Moreover, the latent correlation of images might be disregarded in CNN feature learning and thereby influence the representative capability of the CNN feature. This study develops an equilibrium optimizer with ensemble learning-based cervical precancerous lesion classification on colposcopy images (EOEL-PCLCCI) technique. The presented EOEL-PCLCCI technique mainly focuses on identifying and classifying cervical cancer on colposcopy images. In the presented EOEL-PCLCCI technique, the DenseNet-264 architecture is used for the feature extractor, and the EO algorithm is applied as a hyperparameter optimizer. An ensemble of weighted voting classifications, namely long short-term memory (LSTM) and gated recurrent unit (GRU), is used for the classification process. A widespread simulation analysis is performed on a benchmark dataset to depict the superior performance of the EOEL-PCLCCI approach, and the results demonstrated the betterment of the EOEL-PCLCCI algorithm over other DL models.

Pap Smear Images Classification Using Machine Learning: A Literature Matrix

Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for advanced cervical screening is a highly effective precancerous cell detection technology based on cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in medical imaging have benefited greatly from extraordinary developments in artificial intelligence (AI) technology. However, resource and computational cost constraints prevent the widespread use of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the related studies that have been done by previous researchers related to the automation of cervical cancer classification based on machine learning. The objective of this study is to systematically review and analyses the current research on the classification of the cervical using machine learning. The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for the published paper access until October 2022, this study assessed past approaches for cervical cell classification based on machine learning applications.

Cervical Cell Image Classification-Based Knowledge Distillation

Current deep-learning-based cervical cell classification methods suffer from parameter redundancy and poor model generalization performance, which creates challenges for the intelligent classification of cervical cytology smear images. In this paper, we establish a method for such classification that combines transfer learning and knowledge distillation. This new method not only transfers common features between different source domain data, but also realizes model-to-model knowledge transfer using the unnormalized probability output between models as knowledge. A multi-exit classification network is then introduced as the student network, where a global context module is embedded in each exit branch. A self-distillation method is then proposed to fuse contextual information; deep classifiers in the student network guide shallow classifiers to learn, and multiple classifier outputs are fused using an average integration strategy to form a classifier with strong generalization performance. The experimental results show that the developed method achieves good results using the SIPaKMeD dataset. The accuracy, sensitivity, specificity, and F-measure of the five classifications are 98.52%, 98.53%, 98.68%, 98.59%, respectively. The effectiveness of the method is further verified on a natural image dataset.

Cervical Cell/Clumps Detection in Cytology Images Using Transfer Learning

Cervical cancer is one of the most common and deadliest cancers among women and poses a serious health risk. Automated screening and diagnosis of cervical cancer will help improve the accuracy of cervical cell screening. In recent years, there have been many studies conducted using deep learning methods for automatic cervical cancer screening and diagnosis. Deep-learning-based Convolutional Neural Network (CNN) models require large amounts of data for training, but large cervical cell datasets with annotations are difficult to obtain. Some studies have used transfer learning approaches to handle this problem. However, such studies used the same transfer learning method that is the backbone network initialization by the ImageNet pre-trained model in two different types of tasks, the detection and classification of cervical cell/clumps. Considering the differences between detection and classification tasks, this study proposes the use of COCO pre-trained models when using deep learning methods for cervical cell/clumps detection tasks to better handle limited data set problem at training time. To further improve the model detection performance, based on transfer learning, we conducted multi-scale training according to the actual situation of the dataset. Considering the effect of bounding box loss on the precision of cervical cell/clumps detection, we analyzed the effects of different bounding box losses on the detection performance of the model and demonstrated that using a loss function consistent with the type of pre-trained model can help improve the model performance. We analyzed the effect of mean and std of different datasets on the performance of the model. It was demonstrated that the detection performance was optimal when using the mean and std of the cervical cell dataset used in the current study. Ultimately, based on backbone Resnet50, the mean Average Precision (mAP) of the network model is 61.6% and Average Recall (AR) is 87.7%. Compared to the current values of 48.8% and 64.0% in the used dataset, the model detection performance is significantly improved by 12.8% and 23.7%, respectively.