Computer-aided Analysis Systems Research Articles

Bacterial Disease Detection of Cherry Plant Using Deep Features

Although the cherry plant is widely grown in the world and Turkey, it is a fruit tree that is difficult to grow and maintain. It can be exposed to various pesticide diseases, especially during fruiting. Today, approaches based on expert reviews and analyses are used for the identification of these diseases. In addition, cherry producers are trying to detect diseases with their knowledge based on experience. Computer-aided agricultural analysis systems are also being developed depending on the rapid developments in technology. These systems help to monitor all processes from planting, cultivation, and harvesting of agricultural products and to make decisions to grow the products healthily. One of the most important issues to be detected and monitored with these systems is plant diseases. The features of the cherry plant disease will be determined by using a pre-trained convolutional neural network (CNN) model which is DarkNet-19, within the scope of this study. These machine learning-based features have been used for the detection of bacteria-based diseases commonly seen on the leaves of cherry plants. The acquired features are classified with Linear Discriminant Analysis, K-Nearest Neighbor, and Support Vector Machine classifiers to solve the multi-class problem including diseased (less and very) and healthy plants. The experimental results show that a success rate of 88.1% was obtained in the detection of the disease.

Hybrid convolutional neural network and multilayer perceptron vision transformer model for wheat species classification task: E-ResMLP+

Wheat plant is one of the most basic food sources for the whole world. There are many species of wheat that differ according to the conditions of the region where they are grown. In this context, wheat species can exhibit different characteristics. Issues such as resistance to geographical conditions and productivity are at the forefront in this plant as in all other plants. The wheat species should be correctly distinguished for correct agricultural practice. In this study, a hybrid model based on the Vision Transformer (VT) approach and the Convolutional Neural Network (CNN) model was developed to classify wheat species. For this purpose, ResMLP architecture was modified and the EfficientNetV2b0 model was fine-tuned and improved. A hybrid transformer model has been developed by combining these two methods. As a result of the experiments, the overall accuracy performance has been determined as 98.33%. The potential power of the proposed method for computer-aided agricultural analysis systems is demonstrated.

An Unsupervised Fundus Image Enhancement Method with Multi-Scale Transformer and Unreferenced Loss

Color fundus images are now widely used in computer-aided analysis systems for ophthalmic diseases. However, fundus imaging can be affected by human, environmental, and equipment factors, which may result in low-quality images. Such quality fundus images will interfere with computer-aided diagnosis. Existing methods for enhancing low-quality fundus images focus more on the overall visualization of the image rather than capturing pathological and structural features at the finer scales of the fundus image sufficiently. In this paper, we design an unsupervised method that integrates a multi-scale feature fusion transformer and an unreferenced loss function. Due to the loss of microscale features caused by unpaired training, we construct the Global Feature Extraction Module (GFEM), a combination of convolution blocks and residual Swin Transformer modules, to achieve the extraction of feature information at different levels while reducing computational costs. To improve the blurring of image details caused by deep unsupervised networks, we define unreferenced loss functions that improve the model’s ability to suppress edge sharpness degradation. In addition, uneven light distribution can also affect image quality, so we use an a priori luminance-based attention mechanism to improve low-quality image illumination unevenness. On the public dataset, we achieve an improvement of 0.88 dB in PSNR and 0.024 in SSIM compared to the state-of-the-art methods. Experiment results show that our method outperforms other deep learning methods in terms of vascular continuity and preservation of fine pathological features. Such a framework may have potential medical applications.

AI-Based Decision Support System for Traumatic Brain Injury: A Survey.

Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.

Problems of creating predictive models of the COVID19 coronavirus pandemic

Relevance. The article is devoted to creating prognostic models based on epidemiological and immunological data. Objective: to study the comparative dynamic epidemiological and immunological characteristics of patients with COVID-19. Materials and methods. Methodological approaches to the use of system analysis of epidemiological and immunological characteristics of patients with COVID-19 using multivariate analysis are described. The used technologies of computer-aided analysis systems, algorithms for recognizing, measuring and identifying the condition of patients, and methods of statistical data processing made it possible to create a universal information predictive model for calculating the dynamics of infectious diseases prone to generalization (pandemics), as well as to understand in which groups these new infectious diseases are most dangerous. Results and discussion. Using the methods of system analysis, the epidemiological and immunological aspects of predictive models of the coronavirus pandemic were evaluated using the most objective international data, which increased the information content of the analysis. Conclusions . Creating predictive epidemiological and immunological models of the pandemic is an urgent and promising task to combat the medical and social consequences of the spread of coronavirus infection in Russia.

Classification of Haploid and Diploid Maize Seeds based on Pre-Trained Convolutional Neural Networks

Analysis of agricultural products is an important area that is widely emphasized today. In this context, with the development of technology, computer-aided analysis systems are also being developed. In this study, a system has been proposed for classifying maize seeds as haploid and diploid using pre-trained convolutional neural networks. For this purpose, AlexNet, GoogLeNet, ResNet-18, ResNet-50, and VGG-16 pre-trained models have been used as feature extractors for the haploid and diploid seed classification process. In the first stage, the deep features of haploid and diploid maize seeds have been obtained in these models. The features have been taken from different layers of network architecture. Instead of softmax classifier in the last layer of the network, classifiers based on decision tree, k-nearest neighbor, and support vector machine have been used. According to the classification results with these features, the achievements in network architectures and classifier methods have been observed. The experiments have been carried out on a publicly available dataset consisting of 3000 haploid and diploid maize seed images. The experimental results revealed that the developed classification systems demonstrate a remarkable performance.

Leukocyte Image Segmentation Based on Adaptive Histogram Thresholding and Contour Detection

Aims: The proposed method falls into the category of medical image processing. Background: Computer-aided automatic analysis systems for the analysis and cytometry of leukocyte (White Blood Cells, WBCs) in human blood smear images are a powerful diagnostic tool for many types of diseases, such as anemia, malaria, syphilis, heavy metal poisoning, and leukemia. Leukocyte segmentation is a basis of its automatic analysis, and the segmentation accuracy will directly influence the reliability of image-based automatic leukocyte analysis. Objective: This paper aims to present a leukocyte segmentation method, which improves segmentation accuracy under rapid and standard staining conditions. Methods: The proposed method first localizes leukocytes by color component combination and Adaptive Histogram Thresholding (AHT), and crops sub-image corresponding to each leukocyte. Then, the proposed method employs AHT to extract the nucleus of leukocyte and utilizes image color features to remove image backgrounds such as red blood cells and dyeing impurities. Finally, Canny edge detection is performed to extract the entire leukocyte. Accordingly, cytoplasm is obtained by subtracting nucleus with leukocyte. Results: Experimental results on two datasets containing 160 leukocyte images show that the proposed method obtains more accurate segmentation results than their counterparts. Conclusion: The proposed method obtains more accurate segmentation results than their counterparts under rapid and standard staining conditions.

Barrett’s Epithelial Thickness, Assessed by Volumetric Laser Endomicroscopy, Is Associated With Response to Radiofrequency Ablation

Radiofrequency ablation (RFA) is the most common treatment for flat Barrett's esophagus (BE), but reasons for varying outcomes are poorly understood. A recently developed contrast-enhancement algorithm allows reliable measurement of Barrett's epithelial thickness (BET) from volumetric laser endomicroscopy (VLE) images and correlation with response to RFA. Using this algorithm, we investigated whether patients with thicker Barrett's mucosa are less likely to respond to RFA. In the future, this algorithm may guide choice of RFA dosing or endoscopic resection. We performed a retrospective analysis on all patients with BE who received a baseline VLE scan between May 2015 and October 2016, followed by RFA and 1 follow-up exam, from 14 institutions participating in the United States VLE registry. We measured BET on equidistant locations by estimating the distance between the esophageal surface and the superficial edge of the deepest lamina propria. The primary outcome variable was the percentage reduction in Prague length; secondary outcome variables were complete remission of intestinal metaplasia (CRIM) and presence of strictures after 12 months. Images from 61 patients were included in our final analysis. Mean BET per patient ranged from 224 μm to 705 μm. A 100 μm thicker mean BET per patient resulted in a 12% lower response to treatment, measured by a reduction of Prague length (P = .03), after adjustment for confounders. We found an association between mean BET and CRIM, but not with stricture formation. Based on measurements on contrast-enhanced VLE images, we found that BET correlates with response to RFA. For clinical implementation, larger studies with a standardized follow-up and development of computer-aided image analysis systems are needed.

Targeted therapies for congenital myasthenic syndromes: systematic review and steps towards a treatabolome.

Despite recent scientific advances, most rare genetic diseases — including most neuromuscular diseases — do not currently have curative gene-based therapies available. However, in some cases, such as vitamin, cofactor or enzyme deficiencies, channelopathies and disorders of the neuromuscular junction, a confirmed genetic diagnosis provides guidance on treatment, with drugs available that may significantly alter the disease course, improve functional ability and extend life expectancy. Nevertheless, many treatable patients remain undiagnosed or do not receive treatment even after genetic diagnosis. The growth of computer-aided genetic analysis systems that enable clinicians to diagnose their undiagnosed patients has not yet been matched by genetics-based decision-support systems for treatment guidance. Generating a ‘treatabolome’ of treatable variants and the evidence for the treatment has the potential to increase treatment rates for treatable conditions. Here, we use the congenital myasthenic syndromes (CMS), a group of clinically and genetically heterogeneous but frequently treatable neuromuscular conditions, to illustrate the steps in the creation of a treatabolome for rare inherited diseases. We perform a systematic review of the evidence for pharmacological treatment of each CMS type, gathering evidence from 207 studies of over 1000 patients and stratifying by genetic defect, as treatment varies depending on the underlying cause. We assess the strength and quality of the evidence and create a dataset that provides the foundation for a computer-aided system to enable clinicians to gain easier access to information about treatable variants and the evidence they need to consider.

Automated breast-region segmentation in the axial breast MR images

PurposeThe purpose of this study was to develop a robust breast-region segmentation method independent from the visible contrast between the breast region and surrounding chest wall and skin. Materials and methodsA fully-automated method for segmentation of the breast region in the axial MR images is presented relying on the edge map (EM) obtained by applying a tunable Gabor filter which sets its parameters according to the local MR image characteristics to detect non-visible transitions between different tissues having a similar MRI signal intensity. The method applies the shortest-path search technique by incorporating a novel cost function using the EM information within the border-search area obtained based on the border information from the adjacent slice. It is validated on 52 MRI scans covering the full American College of Radiology Breast Imaging-Reporting and Data System (BI-RADS) breast-density range. ResultsThe obtained results indicate that the method is robust and applicable for the challenging cases where a part of the fibroglandular tissue is connected to the chest wall and/or skin with no visible contrast, i.e. no fat presence, between them compared to the literature methods proposed for the axial MR images. The overall agreement between automatically- and manually-obtained breast-region segmentations is 96.1% in terms of the Dice Similarity Coefficient, and for the breast-chest wall and breast-skin border delineations it is 1.9mm and 1.2mm, respectively, in terms of the Mean-Deviation Distance. ConclusionThe accuracy, robustness and applicability for the challenging cases of the proposed method show its potential to be incorporated into computer-aided analysis systems to support physicians in their decision making.

Operational irreversible compression of telemetry information

An approach to improve the speed of telemetry information processing based on the procedure of spectral correlation properties of the telemetered processes verifying, using the mathematical tools of the theory of runs was developed. Implementation of the developed algorithms in special software for processing and analysis of the launcher rocket telemetry information on the active trajectory allows, reducing the redundancy of TMI processing results used as input information to the computer-aided analysis systems, promptly receive standard integrated conclusions about the controlled events occurring during launcher rocket tests.

Drawing Algorithm for Fuzzy Graphs Using the Partition Tree

Fuzzy graphs have been used frequently and effectively as a method for sociogram analysis. A fuzzy graph has the fundamental characteristic of being able to express a variety of relationships between nodes. The drawing of fuzzy graphs has been studied in computer-aided analysis systems with human interfaces and methods using genetic algorithms. However, computer-aided analysis systems with human interfaces do not provide for automatic drawing, while methods using genetic algorithms have the defect of requiring too much execution time for finding a locally optimum solution. To overcome these defects, we propose an algorithm for drawing intelligible and comprehensive fuzzy graphs using a partition tree. This method automatically draws the fuzzy graphwith nodes arranged on the intersections of a latticed space. Since nodes are optimally arranged on the latticed intersections and put together at a nearby position in accordance with the transition of clusters according to cluster levels in the partition tree, drawing the algorithm makes fuzzy relations easier to understand through fuzzy graph representation. Moreover, fuzzy graphs can be drawn faster than by conventional methods. This paper describes the algorithm and its verification by introducing a system implementing the method for displaying fuzzy graphs. Moreover, we have carried out a case study in which a questionnaire has been administered to students, allowing us to analyze human relations quantitatively using a method based on fuzzy theory. Human relations are represented as fuzzy graphs by our algorithm and analyzed using the fuzzy graph.

Automatic Breast-Line and Pectoral Muscle Segmentation

Pre-processing of mammograms is a crucial step in computer-aided analysis systems. The aim of segmentation is to extract a breast region by estimation of a breast skin-line and a pectoral muscle as well as removing radiographic artifacts and the background of the mammogram. Knowledge of the breast contour also allows further analysis of breast abnormalities such as bilateral asymmetry. In this paper we propose a fully automatic algorithm for segmentation of a breast region, based on two types of global image thresholding: the multi-level Otsu and minimizing the measure of fuzziness as well as the gradient estimation and linear regression. The results of our experiments showed that our method can be used to find a breast line and a pectoral muscle accurately

Mammographic masses classification: novel and simple signal analysis method

Breast cancer represents the leading cause of fatality among cancers for women and there is still no known way of preventing this pathology. Computer-aided analysis systems could be very helpful to improve both the sensitivity and the specificity. Presented is a computer-aided diagnosis system for malignant/benign masses classification of regions of interest in mammograms based on wavelet transform decomposition and an artificial neural network classifier is shown. The main novelty of the system is to consider only small 1D signals crossing the region of interest, allowing one to drastically reduce the amount of data to be processed. An experimental analysis performed on a set of images from the DDSM database has shown the effectiveness of the proposed method.

Application of support vector machine for the detection of P- and T-waves in 12-lead electrocardiogram

Electrocardiogram (ECG) is characterized by a recurrent wave sequence of P, QRS and T-wave associated with each beat. The performance of the computer-aided ECG analysis systems depends heavily upon the accurate and reliable detection of these component waves. This paper presents an efficient method for the detection of P- and T-waves in 12-lead ECG using support vector machine (SVM). Digital filtering techniques are used to remove power line interference and base line wander. SVM is used as a classifier for the detection of P- and T-waves. The algorithm is validated using original simultaneously recorded 12-lead ECG recordings from the standard CSE ECG database. Significant detection rate of 95.43% is achieved for P-wave detection and 96.89% for T-wave detection. The method successfully detects all kind of morphologies of P- and T-waves. The on-sets and off-sets of the detected P- and T-waves are found to be within the tolerance limits given in CSE library.

Inhibition of Both Neutral Endopeptidase and Endothelin-Converting Enzyme by SLV306 Reduces Proteinuria and Urinary Albumin Excretion in Diabetic Rats

Diabetic nephropathy is a serious complication of diabetes associated with a poor prognosis which deteriorates to end-stage renal disease. Increased urinary excretion of protein and albumin are early clinical markers for diabetic renal disease and increased risk of cardiovascular disease. Diabetes causes activation of the renal endothelin system inducing renal damage. We analyzed the effects of SLV306, an inhibitor of both neutral endopeptidase and endothelin-converting enzyme, on diabetes-induced alterations of kidney function and morphology in rats with streptozotocin-induced diabetes. The effects of SLV306 (30 mg/kg per day), captopril (10 mg/kg per day), and placebo on urinary protein and albumin excretion as well as on blood pressure were studied in diabetic rats in comparison to non-diabetic control rats. The rats were treated for 20 weeks. At the end of the study kidney morphology was also analyzed using computer-aided image analysis systems. Serum glucose and blood pressure were similar in all diabetic groups. No side-effects were observed with SLV306 and captopril treatment. Protein excretion was 17.3 +/- 3.0 mg/24 hours in untreated diabetic rats. Protein excretion decreased significantly in the SLV306 (4.8 +/- 0.9 mg/24 hours; P = 0.03 vs untreated diabetic rats) as well as in the captopril (5.1 +/- 1.0 mg/24 hours; P = 0.03 vs untreated diabetic rats) -treated diabetic rats. Albumin excretion was 0.51 +/- 0.12 mg/24 hours in the untreated diabetic group and decreased likewise in the SLV306-treated diabetic rats (0.09 +/- 0.03 mg/24 hours; P = 0.04 vs untreated diabetic rats). The captopril-treated diabetic rats showed a strong trend towards reduced albumin excretion (0.12 +/- 0.04 mg/24 hours; P = 0.06 vs untreated diabetic rats). Computer-aided image analysis revealed that renal interstitial matrix content was significantly decreased in diabetic rats treated with either the angiotensin-converting enzyme inhibitor or the neutral endopeptidase/endothelin-converting enzyme inhibitor as compared to untreated diabetic rats. It was found that SLV306 decreases renal matrix protein content as well as protein and albumin excretion in diabetic rats independent of blood pressure. These effects are comparable to those of angiotensinconverting enzyme inhibition.

Tail profile: a more accurate system for analyzing DNA damage using the Comet assay

The Comet assay (single cell gel electrophoresis assay) measures DNA strand breaks in individual cells. In the assay cells are embedded in agarose, lysed, and electrophoresed under low voltage, allowing migration of damaged DNA. The DNA is stained and subsequently viewed with an epifluorescent microscope. If DNA damage has occurred the electrophoresed DNA fragments appear as a diffuse tail behind the nucleus known as a “comet”. Many computer-aided analysis systems are currently in use to quantify the amount of DNA damage that is represented by a comet image. Here, we present a novel method of analysis known as “tail profile”. This method of analysis provides several advantages over currently employed methods, which rely primarily on the “tail moment” method of analysis. We compared the amount of DNA damage reported from both the tail profile and tail moment methods of analysis and observed a 26% ( P<0.0001) increase in damage detected by tail profile across the 10–25 μm range of tail length, where the majority of the relevant comet data is concentrated. We further report that this increase in sensitivity is not only limited to assessing DNA damage, but also to gathering data from DNA repair assays. Furthermore, we demonstrate increased functionality and extended data analysis capabilities with the use of a compressed collection of images called a “comet chip” and through a visual representation of data called a “profile plot”. Use of the custom macros enabled us to detect an unexpected characteristic of the electrophoretic profile, giving us novel insight into the nature of comet analysis. In addition to the increased analytical sensitivity proffered by this system, the tail profile macros are upgradeable and platform independent.

Seminal Fluid Analysis and Sperm Function Testing

The diagnosis of male infertility is a rapidly developing field of investigation. The traditional descriptive approach to semen analysis is still at the heart of the diagnostic workup of male patients and important advances have been made in the standardization of the procedures used to construct the conventional semen profile, as embodied in the WHO handbook. Significant improvements have also been made in the techniques used to assess the quality of sperm motility. While this was once an entirely subjective exercise, the introduction of CASA systems to take objective measurements of the trajectories of human spermatozoa has revolutionized this form of analysis. The speed and accuracy of such systems has permitted detailed analyses of the relationships between sperm movement and sperm function, which has greatly enhanced the diagnostic power of this form of descriptive analysis. Notwithstanding the importance of the descriptive approach to semen analysis, it has also been recognized that to achieve an accurate diagnosis of male infertility such criteria should be supplemented with assays designed to reveal the functional competence of the spermatozoa. Bioassays have therefore been developed to measure such functions as the penetration of cervical mucus, sperm-zona interaction, the acrosome reaction, and sperm-oocyte fusion. All of these assays have been shown to generate information predictive of the fertilizing potential of human spermatozoa in vivo and in vitro. Despite their diagnostic value, the time, expense, and expertise required to run such functional assays has meant that they have not been widely used by infertility specialists. These functional assays are of value, however, in fundamental studies designed to elucidate the biochemical basis of defective sperm function. The first fruits of this research effort are now beginning to appear in the identification of a number of cytoplasmic markers for defective sperm function and the realization that lipid peroxidation plays a key role in the etiology of male infertility. In the wake of these fundamental studies will flow a new generation of biochemical tests for the diagnosis of defective sperm function and, it is hoped, the development of rational therapies with which to treat this condition.

A Workstation Concept for Computer Aided Analysis and Design of Control Systems

The KEDDC software package for computer-aided analysis and control systems design, developed at Ruhr-University, Bochum, F.R.G. is presently being prepared for a supermicro-based workstation implementation. A bit map display plays a key role for the design of a concurrent user environment, where mixed alphanumerics and graphics arc used to handle a comprehensive system of control tools in a natural way for control engineers.