An expert system for the detection of cervical cancer cells using knowledge-based image analyzer

Cervical Cancer Detection Techniques: A Chronological Review

Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included "(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)". Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease's burden on women worldwide.

Recognition and Clinical Diagnosis of Cervical Cancer Cells Based on our Improved Lightweight Deep Network for Pathological Image

Accurate recognition of cervical cancer cells is of great significance to clinical diagnosis, but these existing algorithms are designed by low-level manual feature, and their performance improvements are limited an improved algorithm based on residual neural network is proposed to improve the accuracy of diagnosis. Firstly, momentum parameters are introduced into the training model; secondly, by changing the number of training samples, the recognition rate of the algorithm can be improved. Therefore, aiming at the task of object recognition under resource constrained condition, we optimize the design method of the network structure such as convolution operation, model parameter compression and enhancement of feature expression depth, and design and implement the lightweight network model structure for embedded platform. Our proposed deep network model can reduce the parameters of the model and the resources needed for operation under the condition of guaranteeing the precision. The experimental results show that the lightweight deep model has better performance than that of the existing comparison models, and it can achieve the model accuracy of 94.1% under the condition that the model with fewer parameters on cervical cells data set.

Quantitative Cytologic Descriptors to Differentiate CLL, Sézary, Granular, and Villous Lymphocytes Through Image Analysis

OBJECTIVES

We aimed to find descriptors to identify chronic lymphocytic leukemia (CLL), Sézary, granular, and villous lymphocytes among normal and abnormal lymphocytes in peripheral blood.

METHODS

Image analysis was applied to 768 images from 15 different types of lymphoid cells and monocytes to determine four discriminant descriptors. For each descriptor, numerical scales were obtained using 627 images from 79 patients. An assessment of the four descriptors was performed using smears from 209 new patients.

RESULTS

Cyan correlation of the nucleus identified clumped chromatin, and standard deviation of the granulometric curve of the cyan of the nucleus was specific for cerebriform chromatin. Skewness of the histogram of the u component of the cytoplasm identified cytoplasmic granulation. Hairiness showed specificity for cytoplasmic villi. In the assessment, 96% of the smears were correctly classified.

CONCLUSIONS

The quantitative descriptors obtained through image analysis may contribute to the morphologic identification of the abnormal lymphoid cells considered in this article.

Remote imaging of single cell 3D morphology with ultrafast coherent phonons and their resonance harmonics

Cell morphological analysis has long been used in cell biology and physiology for abnormality identification, early cancer detection, and dynamic change analysis under specific environmental stresses. This work reports on the remote mapping of cell 3D morphology with an in-plane resolution limited by optics and an out-of-plane accuracy down to a tenth of the optical wavelength. For this, GHz coherent acoustic phonons and their resonance harmonics were tracked by means of an ultrafast opto-acoustic technique. After illustrating the measurement accuracy with cell-mimetic polymer films we map the 3D morphology of an entire osteosarcoma cell. The resulting image complies with the image obtained by standard atomic force microscopy, and both reveal very close roughness mean values. In addition, while scanning macrophages and monocytes, we demonstrate an enhanced contrast of thickness mapping by taking advantage of the detection of high-frequency resonance harmonics. Illustrations are given with the remote quantitative imaging of the nucleus thickness gradient of migrating monocyte cells.

Analysis of Different Computational Techniques for Calculating the Polarizability Tensors of Stem Cells with Realistic Three-Dimensional Morphologies

Recently, the National Institute of Standards and Technology has developed a database of three-dimensional (3D) stem cell morphologies grown in ten different scaffolds to study the effect of the cells' environments on their morphologies. The goal of this work is to study the polarizability tensors of these stem cell morphologies, using three independent computational techniques, to quantify the effect of the environment on the electric properties of these cells. We show excellent agreement between the three techniques, validating the accuracy of our calculations. These computational methods allowed us to investigate different meshing resolutions for each stem cell morphology. After validating our results, we use a fast and accurate Pad' approximation formulation to calculate the polarizability tensors of stem cells for any contrast value between their dielectric permittivity and the dielectric permittivity of their environment. We also performed statistical analysis of our computational results to identify which environment generates cells with similar electric properties. The computational analysis and the results reported herein can be used for shedding light on the response of stem cells to electric fields in applications such as dielectrophoresis and electroporation and for calculating the electric properties of similar biological structures with complex 3D shapes.

A Novel Hepatocellular Carcinoma Image Classification Method Based on Voting Ranking Random Forests

This paper proposed a novel voting ranking random forests (VRRF) method for solving hepatocellular carcinoma (HCC) image classification problem. Firstly, in preprocessing stage, this paper used bilateral filtering for hematoxylin-eosin (HE) pathological images. Next, this paper segmented the bilateral filtering processed image and got three different kinds of images, which include single binary cell image, single minimum exterior rectangle cell image, and single cell image with a size of n⁎n. After that, this paper defined atypia features which include auxiliary circularity, amendment circularity, and cell symmetry. Besides, this paper extracted some shape features, fractal dimension features, and several gray features like Local Binary Patterns (LBP) feature, Gray Level Cooccurrence Matrix (GLCM) feature, and Tamura features. Finally, this paper proposed a HCC image classification model based on random forests and further optimized the model by voting ranking method. The experiment results showed that the proposed features combined with VRRF method have a good performance in HCC image classification problem.

Recent advances in morphological cell image analysis

This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed.

Imaging system for visualization and numerical analysis of cancer at stomach and skin tissues

Digital imaging of cancerous cells is instrumental not only in determining the characteristic of the cancer but also monitoring the progress of the disease in the follow up of the patient and adapting the treatment, accordingly. Therefore, we have developed an imaging system to display and layout the characteristics of normal and cancerous cells in an automated way. Image processing techniques are performed on the digitized images of stomach and skin tissues, in order to derive the number of cells, area of an individual cell, and average area of the cells in a certain size of an image window.

Bounded-depth threshold circuits for computer-assisted CT image classification

We present a stochastic algorithm that computes threshold circuits designed to discriminate between two classes of computed tomography (CT) images. The algorithm employs a partition of training examples into several classes according to the average grey scale value of images. For each class, a sub-circuit is computed, where the first layer of the sub-circuit is calculated by a new combination of the Perceptron algorithm with a special type of simulated annealing. The algorithm is evaluated for the case of liver tissue classification. A depth-five threshold circuit (with pre-processing: depth-seven) is calculated from 400 positive (abnormal findings) and 400 negative (normal liver tissue) examples. The examples are of size n=14,161 (119 x 119) with an 8 bit grey scale. On test sets of 100 positive and 100 negative examples (all different from the learning set) we obtain a correct classification close to 99%. The total sequential run-time to compute a depth-five circuit is about 75h up to 230h on a SUN Ultra 5/360 workstation, depending on the width of the threshold circuit at depth-three. In our computational experiments, the depth-five circuits were calculated from three simultaneous runs for depth-four circuits. The classification of a single image is performed within a few seconds.

Logarithmic simulated annealing for X-ray diagnosis

We present a new stochastic learning algorithm and first results of computational experiments on fragments of liver CT images. The algorithm is designed to compute a depth-three threshold circuit, where the first layer is calculated by an extension of the Perceptron algorithm by a special type of simulated annealing. The fragments of CT images are of size 119x119 with eight bit grey levels. From 348 positive (focal liver tumours) and 348 negative examples a number of hypotheses of the type w(1)x(1)+. . .;+w(n)x(n)>/=theta were calculated for n=14161. The threshold functions at levels two and three were determined by computational experiments. The circuit was tested on various sets of 50+50 additional positive and negative examples. For depth-three circuits, we obtained a correct classification of about 97%. The input to the algorithm is derived from the DICOM standard representation of CT images. The simulated annealing procedure employs a logarithmic cooling schedule c(k)=Gamma/ln(k+2), where Gamma is a parameter that depends on the underlying configuration space. In our experiments, the parameter Gamma is chosen according to estimations of the maximum escape depth from local minima of the associated energy landscape.

Evolutionary weighting of image features for diagnosing of CNS tumors

This paper concerns an application of evolutionary feature weighting for diagnosis support in neuropathology. The original data in the classification task are the microscopic images of ten classes of central nervous system (CNS) neuroepithelial tumors. These images are segmented and described by the features characterizing regions resulting from the segmentation process. The final features are in part irrelevant. Thus, we employ an evolutionary algorithm to reduce the number of irrelevant attributes, using the predictive accuracy of a classifier ('wrapper' approach) as an individual's fitness measure. The novelty of our approach consists in the application of evolutionary algorithm for feature weighting, not only for feature selection. The weights obtained give quantitative information about the relative importance of the features. The results of computational experiments show a significant improvement of predictive accuracy of the evolutionarily found feature sets with respect to the original feature set.

The development and implementation of an expert system for the analysis of umbilical cord blood

An assessment of neonatal outcome may be obtained from analysis of blood in the umbilical cord of the infant immediately after delivery. This can provide information on the health of the newborn infant, guide requirements for neonatal care, and is recommended practice of the Royal College of Obstetricians and Gynaecologists. However, there are problems with the technique. Samples frequently contain errors in one or more of the important parameters, preventing accurate interpretation and many clinical staff lack the expert knowledge required to interpret error-free results. In this paper the development and implementation of an expert system to overcome these difficulties is described. The expert system validates results, provides a textual interpretation and archives all results to database for audit, research and medico-legal purposes. The system has now been in routine clinical use for over 3 years in Plymouth, and has also been installed in several other hospitals in the UK. Results are presented in which the types and frequency of errors are established and the user acceptance of the system is determined.