Pap Smear Dataset Research Articles

DeepCerviCancer - Deep Learning-Based Cervical Image Classification using Colposcopy and Cytology Images

INTRODUCTION: Cervical cancer is a deadly malignancy in the cervix, affecting billions of women annually.OBJECTIVES: To develop deep learning-based system for effective cervical cancer detection by combining colposcopy and cytology screening.METHODS: It employs DeepColpo for colposcopy and DeepCyto+ for cytology images. The models are trained on multiple datasets, including the self-collected cervical cancer dataset named Malhari, IARC Visual Inspection with Acetic Acid (VIA) Image Bank, IARC Colposcopy Image Bank, and Liquid-based Cytology Pap smear dataset. The ensemble model combines DeepColpo and DeepCyto+, using machine learning algorithms. RESULTS: The ensemble model achieves perfect recall, accuracy, F1 score, and precision on colposcopy and cytology images from the same patients. CONCLUSION: By combining modalities for cervical cancer screening and conducting tests on colposcopy and cytology images from the same patients, the novel approach achieved flawless results.

PFEMed: Few-shot medical image classification using prior guided feature enhancement

Deep learning-based methods have recently demonstrated outstanding performance on general image classification tasks. As optimization of these methods is dependent on a large amount of labeled data, their application in medical image classification is limited. To address this issue, we propose PFEMed, a novel few-shot classification method for medical images. To extract general and specific features from medical images, this method employs a dual-encoder structure, that is, one encoder with fixed weights pre-trained on public image classification datasets and another encoder trained on the target medical dataset. In addition, we introduce a novel prior-guided Variational Autoencoder (VAE) module to enhance the robustness of the target feature, which is the concatenation of the general and specific features. Then, we match the target features extracted from both the support and query medical image samples and predict the category attribution of the query examples. Extensive experiments on several publicly available medical image datasets show that our method outperforms current state-of-the-art few-shot methods by a wide margin, particularly outperforming MetaMed on the Pap smear dataset by over 2.63%.

Cell image augmentation for classification task using GANs on Pap smear dataset

One of the solutions to the problem of insufficiently large training datasets in image processing is data augmentation. This process artificially extends the size of training datasets to avoid overfitting. Generative Adversarial Networks yield that become increasingly difficult to differentiate from real images, until the differentiation is no longer possible. Thus, artificial images closely resembling original ones can be generated. Inclusion of artificial images contributes to improving the training process. Medical domain is one of the areas where data acquisition is burdened by many procedures, laws, and prohibitions. As a result the potential size of collected datasets is reduced. This article presents the results of training Convolutional Neural Networks on a artificially extended image datasets. The resulting classification accuracy on a cell classification task of models trained with images generated using the proposed method were increased by up to 12.9% in comparison to that of the model trained only with original dataset from the HErlev Pap smear dataset.

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model.

In recent times, a large number of medical images are generated, due to the evolution of digital imaging modalities and computer vision application. Due to variation in the shape and size of the images, the retrieval task becomes more tedious in the large medical databases. So, it is essential in designing an effective automated system for medical image retrieval. In this research study, the input medical images are acquired from new Pap smear dataset, and then, the visible quality of acquired medical images is improved by applying image normalization technique. Furthermore, the hybrid feature extraction is accomplished using histogram of oriented gradients and modified local binary pattern to extract the color and texture feature vectors that significantly reduces the semantic gap between the feature vectors. The obtained feature vectors are fed to the independent condensed nearest neighbor classifier to classify the seven classes of cell images. Finally, relevant medical images are retrieved using chi square distance measure. Simulation results confirmed that the proposed model obtained effective performance in image retrieval in light of specificity, recall, precision, accuracy, and f-score. The proposed model almost achieved 98.88% of retrieval accuracy, which is better compared to other deep learning models such as long short-term memory network, deep neural network, and convolutional neural network.

Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT.

Cervical cell classification has important clinical significance in cervical cancer screening at early stages. However, there are fewer public cervical cancer smear cell datasets, the weights of each classes’ samples are unbalanced, the image quality is uneven, and the classification research results based on CNN tend to overfit. To solve the above problems, we propose a cervical cell image generation model based on taming transformers (CCG-taming transformers) to provide high-quality cervical cancer datasets with sufficient samples and balanced weights, we improve the encoder structure by introducing SE-block and MultiRes-block to improve the ability to extract information from cervical cancer cells images; we introduce Layer Normlization to standardize the data, which is convenient for the subsequent non-linear processing of the data by the ReLU activation function in feed forward; we also introduce SMOTE-Tomek Links to balance the source data set and the number of samples and weights of the images we use Tokens-to-Token Vision Transformers (T2T-ViT) combing transfer learning to classify the cervical cancer smear cell image dataset to improve the classification performance. Classification experiments using the model proposed in this paper are performed on three public cervical cancer datasets, the classification accuracy in the liquid-based cytology Pap smear dataset (4-class), SIPAKMeD (5-class), and Herlev (7-class) are 98.79%, 99.58%, and 99.88%, respectively. The quality of the images we generated on these three data sets is very close to the source data set, the final averaged inception score (IS), Fréchet inception distance (FID), Recall and Precision are 3.75, 0.71, 0.32 and 0.65 respectively. Our method improves the accuracy of cervical cancer smear cell classification, provides more cervical cell sample images for cervical cancer-related research, and assists gynecologists to judge and diagnose different types of cervical cancer cells and analyze cervical cancer cells at different stages, which are difficult to distinguish. This paper applies the transformer to the generation and recognition of cervical cancer cell images for the first time.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11042-022-12670-0.

Model optimisation of class imbalanced learning using ensemble classifier on over-sampling data

<span lang="EN-US">Data imbalance is one of the problems in the application of machine learning and data mining. Often this data imbalance occurs in the most essential and needed case entities. Two approaches to overcome this problem are the data level approach and the algorithm approach. This study aims to get the best model using the pap smear dataset that combined data levels with an algorithmic approach to solve data imbalanced. The laboratory data mostly have few data and imbalance. Almost in every case, the minor entities are the most important and needed. Over-sampling as a data level approach used in this study is the synthetic minority oversampling technique-nominal (SMOTE-N) and adaptive synthetic-nominal (ADASYN-N) algorithms. The algorithm approach used in this study is the ensemble classifier using AdaBoost and bagging with the classification and regression tree (CART) as learner-based. The best model obtained from the experimental results in accuracy, precision, recall, and f-measure using ADASYN-N and AdaBoost-CART.</span>

Deep Learning based Cervical Cancer Classification and Segmentation from Pap Smears Images using an EfficientNet

One of the most prevalent cancers in the world, cervical cancer claims the lives of many people every year. Since early cancer diagnosis makes it easier for patients to use clinical applications, cancer research is crucial. The Pap smear is a useful tool for early cervical cancer detection, although the human error is always a risk. Additionally, the procedure is laborious and time-consuming. By automatically classifying cervical cancer from Pap smear images, the study's goal was to reduce the risk of misdiagnosis. For picture enhancement in this study, contrast local adaptive histogram equalization (CLAHE) was employed. Then, from this cervical image, features including wavelet, morphological features, and Grey Level Co-occurrence Matrix (GLCM) are extracted. An effective network trains and tests these derived features to distinguish between normal and abnormal cervical images by using EfficientNet. On the aberrant cervical picture, the SegNet method is used to identify and segment the cancer zone. Specificity, accuracy, positive predictive value, Sensitivity, and negative predictive value are all utilized to analyze the suggested cervical cancer detection system performances. When used on the Herlev benchmark Pap smear dataset, results demonstrate that the approach performs better than many of the existing algorithms.

Lightweight convolutional neural network with knowledge distillation for cervical cells classification

Cervical cancer is among the most lethal human malignancies. Women in developing countries are overwhelmingly vulnerable to the cervical cancer because of the lack of medical resources. Although artificial intelligence technologies have witnessed some great progress in healthcare and medical practices, most studies in cervical cells classification focus on improving the accuracy without considering the resource limitations. To build a compact and effective model that meets design requirements for embedded devices, a lightweight convolutional neural network (CNN) architecture is chosen for establishing a highly efficient model with minimal parameters and calculations. Furthermore, knowledge distillation is utilized to improve the representation power of the lightweight CNN. This paper also investigates the importance of model selection in the proposed method. Experiments are conducted on the Herlev Pap smear dataset for the fine-grained 7-class classification tasks. The lightweight Xception, MobileNet and MobileNetV2 all achieved enhanced results (Xception from 71.39% to 72.25%, MobileNet from 62.12% to 64.14%, MobileNetV2 from 60.92% to 61.20%), and the best performing Xception model can achieve a comparable accuracy (72.25% compared to 73.58%) with only 40% of the model size of the powerful Inception-ResnetV2 model. Results shown that the proposed method can be used to develop a lightweight CNN model with improved accuracy, which is believed to be the first in this area of studying cervical cells classification tasks under limited resources. Furthermore, the compact model can potentially lead to a more economical and effective computer-aided system towards the diagnosis and prevention of the cervical cancer.

Deep Auto Encoder Based Extreme Learning System for Automatic Segmentation of Cervical Cells

Nowadays, cervical cancer has emerged as one of the major causes of death and incredibly difficult to identify. In this research, three novel methods have been developed for the automatic detection and classification of cervical cancer in Pap images. There are five steps in this framework which includes data collection, preprocessing, segmentation, feature extraction, and classification of images. The proposed method is implemented using Herlev Pap smear data set. The input image from the data set is pre-processed by using an Anisotropic Diffusion Filter with Unsharp Masking technique that removes the noise in the Pap data. In the next step, an enhanced image sequence is segmented automatically by the proposed Advance Map-Based Superpixel Segmentation (AMBSS) algorithm. Finally, cervix cancer images are procured by utilizing an AMBSS and classified by Support Vector Machine classifier. The accuracy obtained is 85.4%. In order to improve the accuracy AMBSS with quasi newton-based Feed Forward Neural Network classification is used and the accuracy of 96.0% is obtained. Furthermore, AMBSS with Deep auto encoder-based Extreme Learning Machine classification is performed and achieved the accuracy of 99.1%. The findings show that the precision of classification typically exceeds other intelligent methods previously applied.

A fuzzy rank-based ensemble of CNN models for classification of cervical cytology

Cervical cancer affects more than 0.5 million women annually causing more than 0.3 million deaths. Detection of cancer in its early stages is of prime importance for eradicating the disease from the patient’s body. However, regular population-wise screening of cancer is limited by its expensive and labour intensive detection process, where clinicians need to classify individual cells from a stained slide consisting of more than 100,000 cervical cells, for malignancy detection. Thus, Computer-Aided Diagnosis (CAD) systems are used as a viable alternative for easy and fast detection of cancer. In this paper, we develop such a method where we form an ensemble-based classification model using three Convolutional Neural Network (CNN) architectures, namely Inception v3, Xception and DenseNet-169 pre-trained on ImageNet dataset for Pap stained single cell and whole-slide image classification. The proposed ensemble scheme uses a fuzzy rank-based fusion of classifiers by considering two non-linear functions on the decision scores generated by said base learners. Unlike the simple fusion schemes that exist in the literature, the proposed ensemble technique makes the final predictions on the test samples by taking into consideration the confidence in the predictions of the base classifiers. The proposed model has been evaluated on two publicly available benchmark datasets, namely, the SIPaKMeD Pap Smear dataset and the Mendeley Liquid Based Cytology (LBC) dataset, using a 5-fold cross-validation scheme. On the SIPaKMeD Pap Smear dataset, the proposed framework achieves a classification accuracy of 98.55% and sensitivity of 98.52% in its 2-class setting, and 95.43% accuracy and 98.52% sensitivity in its 5-class setting. On the Mendeley LBC dataset, the accuracy achieved is 99.23% and sensitivity of 99.23%. The results obtained outperform many of the state-of-the-art models, thereby justifying the effectiveness of the same. The relevant codes of this proposed model are publicly available on GitHub.

Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection

Cervical cancer is one of the most deadly and common diseases among women worldwide. It is completely curable if diagnosed in an early stage, but the tedious and costly detection procedure makes it unviable to conduct population-wise screening. Thus, to augment the effort of the clinicians, in this paper, we propose a fully automated framework that utilizes deep learning and feature selection using evolutionary optimization for cytology image classification. The proposed framework extracts deep feature from several convolution neural network (CNN) models and uses a two-step feature reduction approach to ensure reduction in computation cost and faster convergence. The features extracted from the CNN models form a large feature space whose dimensionality is reduced using principal component analysis while preserving 99% of the variance. A non-redundant, optimal feature subset is selected from this feature space using an evolutionary optimization algorithm, the grey wolf optimizer, thus improving the classification performance. Finally, the selected feature subset is used to train an support vector machine classifier for generating the final predictions. The proposed framework is evaluated on three publicly available benchmark datasets: Mendeley Liquid Based Cytology (4-class) dataset, Herlev Pap Smear (7-class) dataset, and the SIPaKMeD Pap Smear (5-class) dataset achieving classification accuracies of 99.47, 98.32 and 97.87%, respectively, thus justifying the reliability of the approach. The relevant codes for the proposed approach can be found in: https://github.com/DVLP-CMATERJU/Two-Step-Feature-Enhancement .

Improving Computer-Aided Cervical Cells Classification Using Transfer Learning Based Snapshot Ensemble

Cervical cells classification is a crucial component of computer-aided cervical cancer detection. Fine-grained classification is of great clinical importance when guiding clinical decisions on the diagnoses and treatment, which remains very challenging. Recently, convolutional neural networks (CNN) provide a novel way to classify cervical cells by using automatically learned features. Although the ensemble of CNN models can increase model diversity and potentially boost the classification accuracy, it is a multi-step process, as several CNN models need to be trained respectively and then be selected for ensemble. On the other hand, due to the small training samples, the advantages of powerful CNN models may not be effectively leveraged. In order to address such a challenging issue, this paper proposes a transfer learning based snapshot ensemble (TLSE) method by integrating snapshot ensemble learning with transfer learning in a unified and coordinated way. Snapshot ensemble provides ensemble benefits within a single model training procedure, while transfer learning focuses on the small sample problem in cervical cells classification. Furthermore, a new training strategy is proposed for guaranteeing the combination. The TLSE method is evaluated on a pap-smear dataset called Herlev dataset and is proved to have some superiorities over the exiting methods. It demonstrates that TLSE can improve the accuracy in an ensemble manner with only one single training process for the small sample in fine-grained cervical cells classification.

A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network

The diagnosis of cervical dysplasia, carcinoma in situ and confirmed carcinoma cases is more easily perceived by commercially available and current research-based decision support systems when the scenario of pathologists to patient ratio is small. The treatment modalities for such diagnosis rely exclusively on precise identification of dysplasia stages as followed by The Bethesda System. The classification based on The Bethesda System is a multiclass problem, which is highly relevant and vital. Reliance on image interpretation, when done manually, introduces inter-observer variability and makes the microscope observation tedious and time-consuming. Taking this into account, a computer-assisted screening system built on deep learning can significantly assist pathologists to screen with correct predictions at a faster rate. The current study explores six different deep convolutional neural networks- Alexnet, Vggnet (vgg-16 and vgg-19), Resnet (resnet-50 and resnet-101) and Googlenet architectures for multi-class (four-class) diagnosis of cervical pre-cancerous as well as cancer lesions and incorporates their relative assessment. The study highlights the addition of an ensemble classifier with three of the best deep learning models for yielding a high accuracy multi-class classification. All six deep models including ensemble classifier were trained and validated on a hospital-based pap smear dataset collected through both conventional and liquid-based cytology methods along with the benchmark Herlev dataset.

Biomedical image classification based on a feature concatenation and ensemble of deep CNNs

Deep learning and more specifically Convolutional Neural Network (CNN) is a cutting edge technique which has been applied to many fields including biomedical image classification. To further improve the classification performance for biomedical images, in this paper, a feature concatenation method and a feature concatenation and ensemble method are proposed to combine several CNNs with different depths and structures. Three datasets, namely 2D Hela dataset, PAP smear dataset, and Hep-2 cell image dataset, are used as benchmarks for testing the proposed methods. It is shown from experiments that the feature concatenation and ensemble method outperforms each individual CNN, and the feature concatenation method, as well as several state-of-the-art methods in terms of classification accuracy.

A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images

BackgroundCervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images.MethodScene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm.ResultsThe evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%’, ‘97.64%, 98.08% and 97.16%’ and ‘95.00%, 100% and 90.00%’ were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab.ConclusionsThe major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5–10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.

Segmentation and Classification of Cervical Cells Using Deep Learning

Cervical cancer is the fourth most prevalent disease in women. Accurate and timely cancer detection can save lives. Automatic and reliable cervical cancer detection methods can be devised through the accurate segmentation and classification of Pap smear cell images. This paper presents an approach to whole cervical cell segmentation using a mask regional convolutional neural network (Mask R-CNN) and classifies this using a smaller Visual Geometry Group-like Network (VGG-like Net). ResNet10 is used to make full use of spatial information and prior knowledge as the backbone of the Mask R-CNN. We evaluate our proposed method on the Herlev Pap Smear dataset. In the segmentation phase, when Mask R-CNN is applied on the whole cell, it outperforms the previous segmentation method in precision (0.92±0.06), recall (0.91±0.05) and ZSI (0.91±0.04). In the classification phase, VGG-like Net is applied on the whole segmented cell and yields a sensitivity score of more than 96% with low standard deviation (±2.8%) for the binary classification problem and yields a higher result of more than 95% with low standard deviation (maximum 4.2% in accuracy measurement) for the 7-class problem in terms of sensitivity, specificity, accuracy, h-mean, and F1 score.

Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm

Abstract Globally, cervical cancer ranks as the fourth most prevalent cancer affecting women. However, it can be successfully treated if detected at an early stage. The Pap smear is a good tool for initial screening of cervical cancer, but there is the possibility of error due to human mistake. Moreover, the process is tedious and time-consuming. The objective of this study was to mitigate the risk of mistake by automating the process of cervical cancer classification from Pap smear images. In this research, contrast local adaptive histogram equalization was used for image enhancement. Cell segmentation was achieved through a Trainable Weka Segmentation classifier, and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy c-means algorithm. The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and Pap smear slide images from a pathology unit). An overall classification accuracy, sensitivity and specificity of ‘98.88%, 99.28% and 97.47%‘, ‘97.64%, 98.08% and 97.16%’ and ‘96.80%, 98.40% and 95.20%’ were obtained for each dataset respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that was utilized to select cell features that would improve the classification performance, and the number of clusters used during defuzzification and classification. The evaluation and testing conducted confirmed the rationale of the approach taken, which is based on the premise that the selection of salient features embeds sufficient discriminatory information that leads to an increase in the accuracy of cervical cancer classification. Results show that the method outperforms many of the existing algorithms in terms of the false negative rate (0.72%), false positive rate (2.53%), and classification error (1.12%), when applied to the DTU/Herlev benchmark Pap smear dataset. The approach articulated in this paper is applicable to many Pap smear analysis systems, but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.

DeepPap: Deep Convolutional Networks for Cervical Cell Classification.

Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells-without prior segmentation-based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pretrained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively resampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.