Cells In Pap Smear Images Research Articles

A Semantic Segmentation of Nucleus and Cytoplasm in Pap-smear Images using Modified U-Net Architecture

Pap-smear images can help early detection of cervical cancer, but the manual interpretation by a pathologist can be time-consuming and prone to human error. Semantic segmentation of the cell nucleus and cytoplasm plays an essential role in Pap smear image analysis for the detection of cervical cancer automatically. This study proposes a modified U-Net architecture by adding batch normalization to each convolution layer. Batch normalization aims to stabilize and accelerate the convergence of the model during training, thus overcoming the vanishing gradient problem. The modified U-Net model achieves high accuracy and low loss during the training process, indicating its ability to learn and recognize patterns in the data. The performance evaluation of the model resulted in 91.4 % accuracy, 79.9 % sensitivity, 87.7 % specificity, 81.7 % F1-score, and 83.7 % precision. The results show that the proposed modification of U-Net architecture with batch normalization improves the segmentation performance for cervical cancer cells in Pap smear images. However, improvement in architecture is still required to increase the ability to overcome overlapping areas between the nucleus, cytoplasm, and background.

Swin-GA-RF: genetic algorithm-based Swin Transformer and random forest for enhancing cervical cancer classification.

Cervical cancer is a prevalent and concerning disease affecting women, with increasing incidence and mortality rates. Early detection plays a crucial role in improving outcomes. Recent advancements in computer vision, particularly the Swin transformer, have shown promising performance in image classification tasks, rivaling or surpassing traditional convolutional neural networks (CNNs). The Swin transformer adopts a hierarchical and efficient approach using shifted windows, enabling the capture of both local and global contextual information in images. In this paper, we propose a novel approach called Swin-GA-RF to enhance the classification performance of cervical cells in Pap smear images. Swin-GA-RF combines the strengths of the Swin transformer, genetic algorithm (GA) feature selection, and the replacement of the softmax layer with a random forest classifier. Our methodology involves extracting feature representations from the Swin transformer, utilizing GA to identify the optimal feature set, and employing random forest as the classification model. Additionally, data augmentation techniques are applied to augment the diversity and quantity of the SIPaKMeD1 cervical cancer image dataset. We compare the performance of the Swin-GA-RF Transformer with pre-trained CNN models using two classes and five classes of cervical cancer classification, employing both Adam and SGD optimizers. The experimental results demonstrate that Swin-GA-RF outperforms other Swin transformers and pre-trained CNN models. When utilizing the Adam optimizer, Swin-GA-RF achieves the highest performance in both binary and five-class classification tasks. Specifically, for binary classification, it achieves an accuracy, precision, recall, and F1-score of 99.012, 99.015, 99.012, and 99.011, respectively. In the five-class classification, it achieves an accuracy, precision, recall, and F1-score of 98.808, 98.812, 98.808, and 98.808, respectively. These results underscore the effectiveness of the Swin-GA-RF approach in cervical cancer classification, demonstrating its potential as a valuable tool for early diagnosis and screening programs.

Layer Selection on Residual Network for Feature Extraction of Pap Smear Images

Pap smear screening test is one of the early prevention efforts to detect cervical cancer. Manual screening tests are still prone to observation errors. This study aims to create a convolutional neural network (CNN) model and support vector machine (SVM) model to identify cervical cancer through pap smear images. The data used are 4049 normal and pathological cervical cells in pap smear images sourced from SIPaKMeD, which were divided into 5 classes based on the level of cancer malignancy. The CNN model is used to extract features on the pap smear image, and SVM is used to carry out the classification. The results of this study are four cervical cancer classification models on pap smear images using Resnet50 and Resnet50V2 architecture and SVM algorithms with different scenarios on freeze and unfreeze of the convolution layer. The classification model with the best performance has an accuracy of 97.09%. CNN model with freezing the convolution layer provides much faster in the pre-trained model and the integration of this model with the SVM as the classifier results in the classification model of cervical cells in pap smear images with high accuracy.

CerviCell-detector: An object detection approach for identifying the cancerous cells in pap smear images of cervical cancer

Cervical cancer is the second most commonly seen cancer in women. It affects the cervix portion of the vagina. The most preferred diagnostic test required for screening for cervical cancer is the pap smear test. Pap smear is a time-consuming test as it requires detailed analysis by expert cytologists. Cytologists can screen around 100 to 1000 slides depending upon the availability of advanced equipment. It requires substantial time and effort to carefully examine each slide, identify and classify cells, and make accurate diagnoses. Prolonged periods of visual inspection can increase the likelihood of human errors, such as overlooking abnormalities or misclassifying cells. The sheer volume of slides to be screened can exacerbate fatigue and impact diagnostic accuracy. Due to this reason Artificial intelligence (AI) based computer-aided diagnosis system for the classification and detection of pap smear images is needed. There are some AI-based solutions proposed in the literature, still, an effective and accurate system is under research. In this paper, we implement a state-of-the-art object detection model with a newly available CRIC dataset which follows the Bethesda system for nomenclature. Object detection models implemented are YOLOv5 which uses the CSPNet backbone, Faster R–CNN which has Region Proposal Network (RPN) and Detectron2 framework created by Facebook AI Research (FAIR) Group. ResNext model is implemented among the available models from Detectron2. The CRIC dataset is preprocessed and augmented using Roboflow tool. The performance measures of Average Precision and mean Average precision over the Intersection over Union (IoU) are used to evaluate the effectiveness of the models. The models performed better for two classes namely Normal and Abnormal compared to six classes from the Bethesda system. The highest mean Average Precision (mAP) is observed on the augmented dataset for YOLOv5 models for binary classification with 83 % mAP with IoU in the range of 0.50–0.95.

Automatic Segmentation of Cervical Cells Based on Star-Convex Polygons in Pap Smear Images.

Cervical cancer is one of the most common cancers that threaten women's lives, and its early screening is of great significance for the prevention and treatment of cervical diseases. Pathologically, the accurate segmentation of cervical cells plays a crucial role in the diagnosis of cervical cancer. However, the frequent presence of adherent or overlapping cervical cells in Pap smear images makes separating them individually a difficult task. Currently, there are few studies on the segmentation of adherent cervical cells, and the existing methods commonly suffer from low segmentation accuracy and complex design processes. To address the above problems, we propose a novel star-convex polygon-based convolutional neural network with an encoder-decoder structure, called SPCNet. The model accomplishes the segmentation of adherent cells relying on three steps: automatic feature extraction, star-convex polygon detection, and non-maximal suppression (NMS). Concretely, a new residual-based attentional embedding (RAE) block is suggested for image feature extraction. It fuses the deep features from the attention-based convolutional layers with the shallow features from the original image through the residual connection, enhancing the network's ability to extract the abundant image features. And then, a polygon-based adaptive NMS (PA-NMS) algorithm is adopted to screen the generated polygon proposals and further achieve the accurate detection of adherent cells, thus allowing the network to completely segment the cell instances in Pap smear images. Finally, the effectiveness of our method is evaluated on three independent datasets. Extensive experimental results demonstrate that the method obtains superior segmentation performance compared to other well-established algorithms.

An Improved Fuzzy Deep Learning (IFDL) model for managing uncertainty in classification of pap-smear cell images

An Improved Fuzzy Deep Learning (IFDL) model for managing uncertainty in classification of pap-smear cell images

Modified metaheuristics with stacked sparse denoising autoencoder model for cervical cancer classification

Cervical cancer is the most commonly diagnosed cancer among women globally, with high mortality rate. For early diagnosis, automated and accurate cervical cancer classification approaches can be developed through effective classification of Pap smear cell images. The current study introduces a novel Modified Firefly Optimization Algorithm with Deep Learning-enabled cervical cancer classification (MFFOA-DL3) model for the classification of Pap Smear Images (PSI). The proposed MFFOA-DL3 model examines the PSI for the existence of cervical cancer cells. To accomplish this, the proposed MFFOA-DL3 model primarily applies Bilateral Filtering (BF)-based noise removal approach to get rid of the noise. Then, Kapur's entropy-based image segmentation technique is applied to determine the affected regions. Moreover, EfficientNet technique is also applied to generate the feature vectors. Finally, MFFOA with Stacked Sparse Denoising Autoencoder (SSDA) model is exploited to classify the PSI. In current study, MFFOA is utilized to appropriately modify the parameters related to SSDA model. The proposed MFFOA-DL3 model was experimentally validated using benchmark dataset. The results attained from extensive comparative analysis highlighted the better performance of MFFOA-DL3 model over other recent approaches.

Classification of Pap-smear cell images using deep convolutional neural network accelerated by hand-crafted features.

The classification of cells extracted from Pap-smears is in most cases done using neural network architectures. Nevertheless, the importance of features extracted with digital image processing is also discussed in many related articles. Decision support systems and automated analysis tools of Pap-smears often use these kinds of manually extracted, global features based on clinical expert opinion. In this paper, a solution is introduced where 29 different contextual features are combined with local features learned by a neural network so that it increases classification performance. The weight distribution between the features is also investigated leading to a conclusion that the numerical features are indeed forming an important part of the learning process. Furthermore, extensive testing of the presented methods is done using a dataset annotated by clinical experts. An increase of 3.2% in F1-Score value can be observed when using the combination of contextual and local features. Clinical Relevance - Analysis of images extracted from digital Pap-test using modern machine learning tools is discussed in many scientific papers. The manual classification of the cells can be time-consuming and expensive which requires a high amount of manual labor. Furthermore the result of the manual classification can also be uncertain due to interobserver variability. Considering these, any result that can lead to a more reliable highly accurate classification method is considered valuable in the field of cervical cancer screening.

Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

Cervical cancer is screened by pap smear methodology for detection and classification purposes. Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues. In this paper, we proposed the first system that it ables to classify the pap smear images into a seven classes problem. Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells. Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine (SVM) classifier. The success of this proposed system in distinguishing between the levels of normal cases with 100% accuracy and 100% sensitivity. On top of that, it can distinguish between normal and abnormal cases with an accuracy of 100%. The high level of abnormality is then studied and classified with a high accuracy. On the other hand, the low level of abnormality is studied separately and classified into two classes, mild and moderate dysplasia, with ∼ 92% accuracy. The proposed system is a built-in cascading manner with five models of polynomial (SVM) classifier. The overall accuracy in training for all cases is 100%, while the overall test for all seven classes is around 92% in the test phase and overall accuracy reaches 97.3%. The proposed system facilitates the process of detection and classification of cervical cells in pap smear images and leads to early diagnosis of cervical cancer, which may lead to an increase in the survival rate in women.

Exemplar pyramid deep feature extraction based cervical cancer image classification model using pap-smear images

Cervical cancer is a common type of cancer in women worldwide. Detection of this type of cancer in the early stages is very important for the treatment process. Early diagnosis/detection is very important for the treatment of cervical cancer. The golden standard of diagnosing cervical cancer is the pap-smear test. To automatically diagnose cervical cancer, machine learning is a good solution and many computer vision/deep learning-based models have been presented in the literature.In this study, an exemplar pyramid deep feature extraction-based method has been proposed for the detection of cervical cancer. The prime purpose of our proposal is to classify cervical cells in pap-smear images for the detection of cancer. SIPaKMeD and Mendeley Liquid Based Cytology (LBC) datasets have been used to develop our exemplar pyramid deep feature generator. The phases/steps of the proposed exemplar pyramid structure-based model are; (i) transfer learning-based feature extraction using DarkNet19 or DarkNet53 networks in an exemplar pyramid structure and the proposed feature generator creates 21,000 features. By deploying Neighborhood Component Analysis (NCA), the most informative/weighted 1000 features from the generated 21,000 features. The selected 1000 features by NCA are classified with the Support Vector Machine (SVM) algorithm. Both 5-fold cross-validation and hold-out validation (80:20) have been utilized as validation techniques. The best accuracies for the SIPaKMeD and Mendeley LBC datasets have been computed as 98.26% and 99.47%, respectively. The obtained results illustrate that the proposed exemplar pyramid model is successful to diagnose cervical cancer using pap-smear images.

Pap smear based cervical cancer detection using residual neural networks deep learning architecture

AbstractDetecting and classifying the Pap smear cell images is important task for cervical cancer identification. In this article, dual tree complex wavelet transform (DTCWT) based modified deep learning algorithm is proposed for classifying the Pap smear cell images into four different cases normal, carcinoma in situ, dysplastic, and superficial. This proposed work consists of data augmentation module, DTCWT module and convolutional neural networks (CNN) module for the automatic classification of Pap smear cell images. The CNN module required huge number of cell images for obtaining high classification rate. Hence, the shearing and flipping functions are used in the data augmentation process to improve the dataset samples for further CNN classification module. Then, the spatial pixel behavior of data augmented images is transferred into multimodal pixels using DTCWT, which creates the sub band coefficients in matrix format. This matrix is trained and classified using ResNet 18 which classifies the source Pap smear cell image into four classes. This developed method is tested on the Pap smear cell images available in open access dataset. The average Pap smear detection index (PDI) of the automatic Pap smear cell image classification system is about 99%.

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification.

In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.

Automated Cell Nuclei Segmentation on Cervical Smear Images Using Structure Analysis

Cervical cancer is a common cancer that affects women around the world, and it is also the most common cancer in the developing countries. The cancer burden has increased due to several factors, such as population growth and ageing. In the early century, the systematization of cervical cancer cells takes some time to process manually, and the result that comes out is also inaccurate. This article presents a new nucleus segmentation on pap smear cell images based on structured analysis or morphological approach. Morphology is a broad set of image processing operations that process images based on shape, size and structure. This operation applies a structural element of the image to create an output image of the same size. The most basic of these operations are dilation and erosion. The results of the numerical analysis indicate that the proposed method achieved about 94.38% (sensitivity), 82.56% (specificity) and 93% (accuracy). Also, the resulting performance was compared to a few existing techniques such as Bradley Method, Nick Method and Sauvola Method. The results presented here may facilitate improvements in the detection method of the pap smear cell image to resolve the time-consuming issue and support better system performance to prevent low precision result of the Human Papilloma Virus (HPV) stages. The main impact of this paper is will help the doctor to identify the patient disease based on Pap smear analysis such as cervical cancer and increase the percentages of accuracy compared to the conventional method. Successful implementation of the nucleus detection techniques on Pap smear image can become a standard technique for the diagnosis of various microbiological infections such as Malaria and Tuberculosis.

Automatic segmentation of nuclei from pap smear cell images: A step toward cervical cancer screening

AbstractCervical cancer is identified as the fourth most recurrent cancer among women across the globe. The cancer is treatable, if identified at the early stage. Pap smear test is the most common and the best tool for initial screening of cancer. Pap smear cell level image analysis is an open issue. The limitation of the analysis is due to the complexity of the cell structure. The smear cell image is composed of cytoplasm and nucleus. The shape and structure of the nucleus determines the cancer prevalence. Segmentation of nucleus is an important step in cancer detection. There are various methods developed for nucleus segmentation. The article proposes multithresholding algorithm to segment cytoplasm and nucleus region from the background. Morphological operations are used for correcting the segmented output. Support vector machine classifier is used for classifying the smear cell as normal or abnormal based on the extracted features of the segmented output. The obtained accuracy of the classifier, sensitivity and specificity for single smear cell are 99.66%, 99.85% and 99.17%.

A Review: Pap Smear Analysis Based on Image Processing Approach

Nowadays, in the hospital, cervical cancer is in the higher rank (number 2) of the most popular cancer among ladies in the world. This cancer develops in the woman’s cervix which the womb is the entrance. Mostly, doctors in the hospital having trouble to identify the cancer cell because the nucleus of the cell is sometimes slightly hard to observe with eyes. The nucleus of the normal cell is in a smaller size compared to the abnormal nucleus. The abnormal nucleus has a bigger size, which sometimes, the size cannot be identified accurately by seeing with bare eyes to classify the stages of cervical cancer. This is because every doctor has different perspectives to observe the classification of the stages of cancer by observing the nucleus without accurate dimensionality reduction in the accuracy of the classifier. Recently, many researchers proposed a method to detect and classify the Pap smear cell images for cervical cancer diagnosis. This approach may improve the accuracy of the detection and the classification which to show better performance with the balance data and samples precisely. Some of the patients got the result that they are on stage 2, however after re-testing, they actually on stage 4 which the chance to heal is very low. This happens because the doctor can’t find the accurate balance data and sample precisely. In this paper, a comprehensive review of cervical detection based on segmentation nucleus and classification was studied.

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.

Inflammatory Cell Extraction in Pap smear Images: A Combination of Distance Criterion and Image Transformation Approach

In order to obtain a diagnosis of cervical cancer information, the characteristics of each cell nucleus must be identified and evaluated properly through a Pap smear test. The presence of inflammatory cells in Pap smear images can complicate the process of identification of cell nuclei in the early detection of cervical cancer. Inflammatory cells need to be eliminated to assist pathologists in reading Pap smear slides. In this work, we developed a novel method to extract the inflammatory cells that allow detection of cell nuclei more accuracy. The proposed algorithm consists of two stages: extraction of inflammatory cells using the distance criterion and image transformation. This experiment applied to the 1358 cells comprising 378 nuclei cells and 980 inflammatory cells from 25 Pap smear images. The results showed that our method can significantly reduce the amount of inflammation that can disrupt the cell nuclei in the detection process. The proposed method has promising results with a sensitivity level of 97% and a specificity of 84.38%.

Computer aided detection and classification of Pap smear cell images using principal component analysis

Pap smear is a screening methodology employed in cervix cancer detection and diagnosis. The Pap smear images of cervical region are used to detect the abnormality of the cervical cells. In this paper, the computer aided automatic detection and classification method for Pap smear cell images are proposed. The cell nucleus is segmented using watershed segmentation methodology and features are extracted from segmented cell nucleus Pap smear image. The extracted features are classified using principal component analysis method. The proposed system classifies the test Pap smear cell image into dysplastic (D), parabasal (P) and superficial (S) cell images for cervical cancer diagnosis.

Segmentation of Overlapping Cervical Cells in Normal Pap Smear Images Using Distance-Metric and Morphological Operation

The automatic interpretation of Pap Smear image is one of challenging issues in some aspects. Accurate segmentation for each cell is an important procedurethat must be done so that no information is lost during the evaluation process. However, the presence of overlapping cells in Pap Smear image make the automated analysis of these cytology images become more difficult. In most ofthe studies, cytoplasm segmentation is the difficult stage because the boundaries between cells are very thin. In this study, we propose an algorithm that can segment the overlapping cytoplasm. First, the morphology operation and global thresholding to segment cytoplasm is done. Second, the overlapping area on cytoplasm region is separated using morphological operation and distance criteria on each pixel. The proposed method has been evaluated against the results of manual tracing by experts. The experiment results show that the proposed method can segment the overlapping cytoplasm as similar as experts do, i.e., 2:897 3:632 (mean std) using Hausdorff distance.

Analysis of Malignancy in Pap Smear Images Using Gray Level Co-Occurrence Matrix and Gradient Magnitude

Hyperchromasia is one of the most common dysplastic change occur in cervical cell images particularly in the nucleus region. The texture of an image is a function of spatial variations of the gray level values and it is used to measure the variations of the pixel intensity of the surface in an image. Gray level co-occurrence matrix (GLCM) is widely used for texture analysis and it shows how repeatedly the different combinations of gray level values occur in an image. Textural changes in the region of interest can be identified by relating the co-occurring pairs of pixels spatially in various orientations and distance. Gradient magnitude and histogram equalization methods are used to enhance the details of the image. Biological variations in the textural region of the nucleus are analyzed and statistical significance at each point on the image is calculated. Various properties like energy, entropy, contrast, homogeneity, correlation and autocorrelation are computed for the classification of Pap smear cell images.