Pap Smear Images Research Articles

A Versatile Detection of Cervical Cancer with i-WFCM and Deep Learning based RBM Classification

One of the most common and curable types of cancer in women is cervical cancer, a common chronic condition. Pap smear images is a common way for screening the cervical cancer. It does not present with symptoms until the disease has advanced stages, cervical cancer cannot be detected in its early stages. Because of this, accurate staging will make it easier to give the patient the right amount of treatment. In this paper proposes the Anisotropic Diffusion Filter has been used to improve the Pap smear image by removing noise and preserving the image's edges. The contrast of a Pap smear image has been enhanced using Histogram Equalization. The enhanced image has been segmented using Improved Weighted Fuzzy C-means clustering to make it easier to identify the effective features. As a result, the effective features are extracted from the segmented region and used by a Restricted Boltzmann Machine classifier based on Deep Learning to classify the cancer. The performance of the proposed cervical cancer detection system can be measured in terms of sensitivity, specificity, F-measure and accuracy. The performance measures for the proposed system can be achieves 95.3% accuracy, 88.6% specificity, 89.13% precision, 88.56% recall, and 89.7% F-measure respectively. Based on simulation results, the proposed method performs better than conventional methods such as RDVLNN, Random Forest (RF), Extreme Learning Machine (ELM), and Support Vector Machine (SVM) for detecting cervical cancer.

Image Segmentation of Normal Pap Smear Thinprep using U-Net with Mobilenetv2 Encoder

Pap smear is a technique to detect changes in the cells in the uterine wall. With a Pap smear, a woman can be known to have cervical cancer or not. However, the problem of cancer screening on pap smear images is largely hindered by improper cell staining and overlapping cell images. For accurate Pap smear image segmentation, this study uses the U-Net method which is better for Pap smear image segmentation. This method integrates the MobilenetV2 network and converts ordinary convolution into deep split convolution to improve transmission and feature utilization by the network, and at the same time increase the speed of feature extraction. Then the segmentation results from MobilenetV2 produce accuracy in distinguishing the nucleus, cytoplasm, and background on the pap smear image. The dataset used in this study is a normal analogue image of the Pap smear image obtained from the RepoMedUNM Database. Initial data processing is done by digitizing the image, where analog data from the Pap smear is transformed into a digital image. Based on the results of research that has been carried out, namely segmentation of Pap smear images using U-Net with MobilenetV2 encoder, the accuracy value on differences in nucleus, cytoplasm, and background cells is 98%.

Medical Image Registration at Pap Smear for Early Identification of Cervical Cancer

The complexity of the cell structure and high overlap causes poor image contrast. Complex imaging factors in lighting differences, dye concentrations, and other variables such as drying air, excess blood, mucus, bacteria, or inflammation can make automatic visual interpretation more difficult. This study proposes an approach model by combining basic image processing techniques in deep learning for segmentation of the nucleus in the Overlap Cell Image of Pap Smear of Cervical Cancer patients. The purpose of this research is to segment by increasing the identification accuracy of Pap smear images on RepomedUNM public data. The results have the best performance as seen in the MSE value, the lowest RMSE value is 0.2024253 and the lowest PSNR is 0.04009707 and the highest PSNR is 65.3826018 dB. So, this study can be used as a reference in identifying the Cervical Cancer Nucleus as Medical Image Registration (MIR) patients.

Optimization of the Preprocessing Method for Edge Detection on Overlapping Cells at PAP Smear Images

The complexity of the cell structure and high overlap cause poor image contrast. Complex imaging factors can make automatic visual interpretation more difficult. Segmentation separates a digital image into different parts with homogeneous attributes so that different areas have different features. The challenges faced in performing nucleus segmentation on Pap Smear (PS) images are poor contrast, the presence of neutrophils, and uneven staining of overlapping cells. This research was conducted to improve image quality in identifying the nucleus accurately. The method used is the Polynomial Contrast Enhancement (PCE) model as an approach to preprocessing. This method functions to change the contrast of the Pap smear image against the overlapping cells so that it becomes a significant contrast in detecting the edge of the nucleus object. The detection process uses the Robert and Prewitt edge detection method to test the identification of the nucleus object on 797 PS Repository images of the University of Nusa Mandiri (RepomedUNM). The accuracy result obtained is 86.8%. Comparing Robert's edge detection and Prewitt's edge detection shows that the PCE approach as a filter method can overcome color contrast problems and detect more accurately. The difficulty in detecting the nucleus from the PS image against the overlapping cells can be solved. This method can distinguish overlapping cells from their core during testing, thus becoming a reference in identifying cells with improved accuracy and testing larger data sets.

Cervical precancerous lesion classification using quantum invasive weed optimization with deep learning on biomedical pap smear images

AbstractBiomedical imaging devices, in general, have been made and used a lot lately to examine the insides of the body during diagnostic and analytic procedures. Biomedical imaging gives accurate information about metabolites, which can be used to find and classify diseases because it is not invasive. For the study of cervical cancer (CC), the pap smear is a crucial type of biological imaging. CC is a crucial reason to enhance the rate of women's mortalities. Proper screening of pap smear images is critical for assisting in the early detection and analysis of CC. Computer‐aided systems for cancerous cell recognition need well established artificial intelligence (AI) methods. In this study, we introduce an automated Cervical Precancerous Lesion Classification using Quantum Invasive Weed Optimization with Deep Learning (CPLC‐QIWODL) on biomedical pap smear images. The presented CPLC‐QIWODL technique examines the pap smear images for cervical cancer classification. To do so, the presented CPLC‐QIWODL technique pre‐processes the biomedical images using a Gabor filtering (GF) approach. Moreover, the CPLC‐QIWODL technique uses a deep convolutional neural network‐based SqueezeNet system for feature extraction. Furthermore, the hyperparameter tuning of the SqueezeNet methodology takes place using the QIWO technique, showing the novelty of the work. Finally, to classify CC, the deep variational autoencoder (DVAE) model is applied. The experimental result analysis of the CPLC‐QIWODL technique is tested using a benchmark medical image database. Extensive comparative results demonstrated the enhanced outcomes of the CPLC‐QIWODL technique over other existing algorithms, with a maximum accuracy of 99.07%.

MSENet: Mean and standard deviation based ensemble network for cervical cancer detection

Cervical cancer is one of the most concerning carcinogenic diseases among women worldwide. The condition is especially bad in low- or middle-income countries due to the lack of medical facilities. In such situations, computer-aided diagnosis (CAD) systems can alleviate the need to a large extent. However, sometimes a single learning model may not be effective enough to capture relevant information for accurate prediction of diseases from complex data. To this end, we propose an ensemble of deep learning models, called Mean and Standard Deviation-based Ensemble Network (MSENet), for detecting cervical cancer from Pap smear images. Our ensemble model consists of three standard base classifiers, namely Xception, Inception V3, and VGG-16. We further improve the classification abilities of these models by implementing a novel probability enhancement scheme that takes into account the mean and standard deviation of the confidence scores. This technique enables the overall framework to capture complementary information offered by the base classifiers and is tailored to the characteristics of deep learners. Finally, we use the product rule to aggregate the obtained outcomes and get final predictions. The proposed MSENet has been evaluated on a standard public benchmark dataset, called SIPaKMeD. With a classification accuracy of 97.21% using a 5-fold cross-validation scheme, the MSENet outperforms many state-of-the-art methods. The source codes are made public in the following Github repository https://github.com/rishavpramanik/msenet.

Hybridization of Deep Learning Pre-Trained Models with Machine Learning Classifiers and Fuzzy Min-Max Neural Network for Cervical Cancer Diagnosis.

Medical image analysis and classification is an important application of computer vision wherein disease prediction based on an input image is provided to assist healthcare professionals. There are many deep learning architectures that accept the different medical image modalities and provide the decisions about the diagnosis of various cancers, including breast cancer, cervical cancer, etc. The Pap-smear test is the commonly used diagnostic procedure for early identification of cervical cancer, but it has a high rate of false-positive results due to human error. Therefore, computer-aided diagnostic systems based on deep learning need to be further researched to classify the pap-smear images accurately. A fuzzy min-max neural network is a neuro fuzzy architecture that has many advantages, such as training with a minimum number of passes, handling overlapping class classification, supporting online training and adaptation, etc. This paper has proposed a novel hybrid technique that combines the deep learning architectures with machine learning classifiers and fuzzy min-max neural network for feature extraction and Pap-smear image classification, respectively. The deep learning pretrained models used are Alexnet, ResNet-18, ResNet-50, and GoogleNet. Benchmark datasets used for the experimentation are Herlev and Sipakmed. The highest classification accuracy of 95.33% is obtained using Resnet-50 fine-tuned architecture followed by Alexnet on Sipakmed dataset. In addition to the improved accuracies, the proposed model has utilized the advantages of fuzzy min-max neural network classifiers mentioned in the literature.

Deep Learning and Transfer Learning Methods to Effectively Diagnose Cervical Cancer from Liquid-Based Cytology Pap Smear Images

As cervical cancer is considered one of the leading causes of death for women globally, different screening techniques have emerged. As the Papanicolaou technique generates high numbers of false negatives due to only testing 20% of a sample, the liquid-based cytology technique was developed to test 100% of the sample and improve accuracy. However, as the larger sample size has made it difficult to detect the lesion images through a microscope, studies have looked for ways to intelligently analyze sample. The aim of this study is to develop an artificial intelligence image recognition system that detects the lesion level of cervical cancer of liquid-based Pap smears under the Bethesda classification of cancer (NI/LSIEL/HSIEL/SCC). For this purpose, six activities were carried out: dataset selection, data augmentation, optimization, model development, evaluation and system construction. A dataset built from publicly available Pap smear images and passed through data augmentation algorithms generated a total of 2,676 images. Two models, ResNet50V2 and ResNet101V2, were developed under Deep Learning and Transfer Learning protocols. The evaluation showed that the ResNet50V2 model obtained better performance, where the classification of HSIL and SCC type images obtained a precision of 0.98 and achieved an accuracy of 0.97. Finally, the system based on the ResNet50V2 model was built and its performance was validated.

Developing a Tuned Three-Layer Perceptron Fed with Trained Deep Convolutional Neural Networks for Cervical Cancer Diagnosis.

Cervical cancer is one of the most common types of cancer among women, which has higher death-rate than many other cancer types. The most common way to diagnose cervical cancer is to analyze images of cervical cells, which is performed using Pap smear imaging test. Early and accurate diagnosis can save the lives of many patients and increase the chance of success of treatment methods. Until now, various methods have been proposed to diagnose cervical cancer based on the analysis of Pap smear images. Most of the existing methods can be divided into two groups of methods based on deep learning techniques or machine learning algorithms. In this study, a combination method is presented, whose overall structure is based on a machine learning strategy, where the feature extraction stage is completely separate from the classification stage. However, in the feature extraction stage, deep networks are used. In this paper, a multi-layer perceptron (MLP) neural network fed with deep features is presented. The number of hidden layer neurons is tuned based on four innovative ideas. Additionally, ResNet-34, ResNet-50 and VGG-19 deep networks have been used to feed MLP. In the presented method, the layers related to the classification phase are removed in these two CNN networks, and the outputs feed the MLP after passing through a flatten layer. In order to improve performance, both CNNs are trained on related images using the Adam optimizer. The proposed method has been evaluated on the Herlev benchmark database and has provided 99.23 percent accuracy for the two-classes case and 97.65 percent accuracy for the 7-classes case. The results have shown that the presented method has provided higher accuracy than the baseline networks and many existing methods.

Pap Smear Image Analysis Based on Nucleus Segmentation and Deep Learning – A Recent Review

Cervical cancer refers to a dangerous and common illness that impacts women worldwide. Moreover, this cancer affects over 300,000 people each year, with one woman diagnosed every minute. It affects over 0.5 million women annually, leading to over 0.3 million deaths. Recently, considerable literature has grown around developing technologies to detect cervical cancer cells in women. Previously, a cervical cancer diagnosis was made manually, which may result in a false positive or negative. Automated detection of cervical cancer and analysis method of the Papanicolaou (Pap) smear images are still debated among researchers. Thus, this paper reviewed several studies related to the detection method of Pap smear images focusing on Nuclei Segmentation and Deep Learning (DL) from the publication year of 2020, 2021, and 2022. Training, validation, and testing stages have all been the subject of study. However, there are still inadequacies in the current methodologies that have caused limitations to the proposed approaches by researchers. This study may inspire other researchers to view the proposed methods' potential and provide a decent foundation for developing and implementing new solutions.

Interpretable pap-smear image retrieval for cervical cancer detection with rotation invariance mask generation deep hashing

Cervical cancer is a common disease in women, affecting their lives negatively and often resulting in death. Pap-smear tests are preferred by doctors as the primary tool in the early diagnosis and treatment of the disease. Physicians can be facilitated in the detection of five different categories of cervical cancer and similar cellular disease cases with the Pap-smear image retrieval technology. In this study, an algorithm for retrieval of cervical cancer images using hash coding with a Convolutional Neural Network (CNN) has been implemented. A sensitive deep hashing method combining interpretable mask generation and rotation invariance is proposed for cervical cancer detection. The distinctive features of cervical cancer cells with complex morphological features are focused on with the proposed hybrid dilated convolution spatial attention module and insignificant features are eliminated. Moreover, the loss function of Cauchy rotation invariance in terms of cervical cancer cell target is presented. In this way, the differences in the input samples are revealed, allowing the CNN to learn from different angles and achieve certain rotation invariance. The versatility and performance of the proposed method, as well as the efficiency of the loss function, have been tested on the SIPaKMeD and Mendeley LBC datasets consisting of cervical cancer images. In the experimental results obtained, it is shown that the proposed spatial attention module and rotational invariance deep hashing network generate high performance in cervical cancer image retrieval problems.

Classification of normal and abnormal overlapped squamous cells in pap smear image

Classification of normal and abnormal overlapped squamous cells in pap smear image

Majority Voting as Ensemble Classifier for Cervical Cancer Classification

Cervical cancer is one of the deadliest female cancers. Early identification of cervical cancer through pap smear cell image evaluation is one of the strategies to reduce cervical cancer cases. The classification methods that are often used are SVM, MLP, and K-NN. The weakness of the SVM method is that it is not efficient on large datasets. Meanwhile, in the MLP method, large amounts of data can increase the complexity of each layer, thereby affecting the duration of the weighting process. Moreover, the K-NN method is not efficient for data with a large number of attributes. The ensemble method is one of the techniques to overcome the limitations of a single classification method. The ensemble classification method combines the performance of several classification methods. This study proposes an ensemble method with the majority voting that can be used in cervical cancer classification based on pap smear images in the Herlev dataset. Majority Voting is used to integrate test results from the SVM, MLP, and KNN methods by looking at the majority results on the test data classification. The results of this study indicate that the accuracy results obtained in the ensemble method increased by 1.72% compared to the average accuracy value in SVM, MLP, and KNN. for sensitivity results, the results of the ensemble method were able to increase the sensitivity increase by 0.74% compared to the average of the three single classification methods. for specificity, the ensemble method can increase the specificity results by 3.4%. From the results of the study, it can be concluded that the ensemble method with the most votes is able to improve the classification performance of the single classification method in classifying cervical cancer abnormalities with pap smear images.

Pap smear-based cervical cancer detection using hybrid deep learning and performance evaluation

ABSTRACT Cervical cancer causes the abnormal growth of the cells at the cervix region of the woman’s uterus. It is the second most common type of cancer after the breast cancer seen in women. Detection of cervical cancer at the early stage is vital. Various screening methods like Pap smear, Colposcopy and HPV testing are carried out for the detection of cervical cancer. The possible screening techniques to diagnose cervical cancer include the visual inspection of the cervix (VIA), Pap smear examination (cytology), Colposcopy, Biopsy and HPV-DNA detection. All these techniques need the involvement of an expert doctor and/or pathologist. Also, a cancer diagnosis is a subjective process where the experience and training of pathologists are significant factors. Intelligent and automated screening systems will be helpful in such scenarios. This paper presents the methodology for cervical cancer prediction based on pap smear images. Pre-trained deep neural network models are used for feature extraction and different machine-learning (ML) models are trained on extracted features. In the proposed methodology, four pre-trained models such as Alexnet, Resnet-18, Resnet-50 and Googlenet are fine-tuned for feature extraction followed by the different ML algorithms. Among these simple logistic regression, the algorithms have performed best with the highest accuracy of 95.14% with the Alexnet pre-trained model.

HDFCN: A Robust Hybrid Deep Network Based on Feature Concatenation for Cervical Cancer Diagnosis on WSI Pap Smear Slides.

Cervical cancer is a critical imperilment to a female's health due to its malignancy and fatality rate. The disease can be thoroughly cured by locating and treating the infected tissues in the preliminary phase. The traditional practice for screening cervical cancer is the examination of cervix tissues using the Papanicolaou (Pap) test. Manual inspection of pap smears involves false-negative outcomes due to human error even in the presence of the infected sample. Automated computer vision diagnosis revamps this obstacle and plays a substantial role in screening abnormal tissues affected due to cervical cancer. Here, in this paper, we propose a hybrid deep feature concatenated network (HDFCN) following two-step data augmentation to detect cervical cancer for binary and multiclass classification on the Pap smear images. This network carries out the classification of malignant samples for whole slide images (WSI) of the openly accessible SIPaKMeD database by utilizing the concatenation of features extracted from the fine-tuning of the deep learning (DL) models, namely, VGG-16, ResNet-152, and DenseNet-169, pretrained on the ImageNet dataset. The performance outcomes of the proposed model are compared with the individual performances of the aforementioned DL networks using transfer learning (TL). Our proposed model achieved an accuracy of 97.45% and 99.29% for 5-class and 2-class classifications, respectively. Additionally, the experiment is performed to classify liquid-based cytology (LBC) WSI data containing pap smear images.

Classification of Cervical-Cancer from Pap Smear Images: A Convolutional Neural Network Approach

Classification of Cervical-Cancer from Pap Smear Images: A Convolutional Neural Network Approach

Privacy Preserved Cervical Cancer Detection Using Convolutional Neural Networks Applied to Pap Smear Images.

Image processing has enabled faster and more accurate image classification. It has been of great benefit to the health industry. Manually examining medical images like MRI and X-rays can be very time-consuming, more prone to human error, and way more costly. One such examination is the Pap smear exam, where the cervical cells are examined in laboratory settings to distinguish healthy cervical cells from abnormal cells, thus indicating early signs of cervical cancer. In this paper, we propose a convolutional neural network- (CNN-) based cervical cell classification using the publicly available SIPaKMeD dataset having five cell categories: superficial-intermediate, parabasal, koilocytotic, metaplastic, and dyskeratotic. CNN distinguishes between healthy cervical cells, cells with precancerous abnormalities, and benign cells. Pap smear images were segmented, and a deep CNN using four convolutional layers was applied to the augmented images of cervical cells obtained from Pap smear slides. A simple yet efficient CNN is proposed that yields an accuracy of 0.9113% and can be successfully used to classify cervical cells. A simple architecture that yields a reasonably good accuracy can increase the speed of diagnosis and decrease the response time, reducing the computation cost. Future researchers can build upon this model to improve the model's accuracy to get a faster and more accurate prediction.

Classification of cervical cancer from Pap smear images: a convolutional neural network approach

Classification of cervical cancer from Pap smear images: a convolutional neural network approach

Hybrid Decision Fusion based Multimodal Ensemble Framework for Cervical Cancer Detection

Cervical cancer is fourth in the list of cancers that affect women. It has remained the main cause of death for women in developing nations. The cancer is spread through human papilloma virus (HPV), which is sexually transmitted. Pap smear and colposcopy image analysis remain prominent methods of diagnosis. These screening tests require skilled diagnostic experts, a scarce resource in developing countries thus restricting the effectiveness of the cancer detection process in large scale. Machine learning and deep learning are branches of artificial intelligence that are being used increasingly in cancer diagnosis. This study proposes a novel hybrid intelligent system for cervical cancer detection. A hybrid model of feature extraction and feature fusion is proposed for merging the two-state image and clinical data. Subsequently a machine learning ensemble learner is assembled to classify the features. The model performed with a satisfactory accuracy of 96.16%. Our results show that our method outperforms state of the art approaches and archives better, dependable accuracy.

Cervical cancer detection using K nearest neighbor imputer and stacked ensemble learningmodel.

Cervical cancer stands as a leading cause of mortality among women in developing nations. To ensure the reduction of its adverse consequences, the primary protocols to be adhered to involve early detection and treatment under the guidance of expert medical professionals. An effective approach for identifying this form of malignancy involves the examination of Pap smear images. However, in the context of automating cervical cancer detection, many of the existing datasets frequently exhibit missing data points, a factor that can substantially impact the effectiveness of machine learning models. In response to these hurdles, this research introduces an automated system designed to predict cervical cancer with a dual focus: adeptly managing missing data while attaining remarkable accuracy. The system's core is built upon a stacked ensemble voting classifier model, which amalgamates three distinct machine learning models, all harmoniously integrated with the KNN Imputer to address the issue of missing values. The model put forth attains an accuracy of 99.41%, precision of 97.63%, recall of 95.96%, and an F1 score of 96.76% when incorporating the KNN imputation method. The investigation conducts a comparative analysis, contrasting the performance of this model with seven alternative machine learning algorithms in two scenarios: one where missing values are eliminated, and another employing KNN imputation. This study offers validation of the effectiveness of the proposed model in comparison to current state-of-the-art methodologies. This research delves into the challenge of handling missing data in the dataset utilized for cervical cancer detection. The findings have the potential to assist healthcare professionals in achieving early detection and enhancing the quality of care provided to individuals affected by cervical cancer.