Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.

Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification in blood smears offers insights to pathologists, aiding diagnosis across medical conditions. Traditional techniques, including manual counting, detection, classification, and visual inspection of microscopic images by medical professionals, pose challenges due to their labor-intensive nature. However, traditional methods are time consuming and sometimes susceptible to errors. Here, we propose a high-performance convolutional neural network (CNN) coupled with a dual-attention network that efficiently detects and classifies WBCs in microscopic thick smear images. The main aim of this study was to enhance clinical hematology systems and expedite medical diagnostic processes. In the proposed technique, we utilized a deep convolutional generative adversarial network (DCGAN) to overcome the limitations imposed by limited training data and employed a dual attention mechanism to improve accuracy, efficiency, and generalization. The proposed technique achieved overall accuracy rates of 99.83%, 99.35%, and 99.60% for the peripheral blood cell (PBC), leukocyte images for segmentation and classification (LISC), and Raabin-WBC benchmark datasets, respectively. Our proposed approach outperforms state-of-the-art methods in terms of accuracy, highlighting the effectiveness of the strategies employed and their potential to enhance diagnostic capabilities and advance real-world healthcare practices and diagnostic systems.

Martius flaps for low rectovaginal fistulae: a systematic review and proportional meta-analysis.

BACKGROUND: Rectovaginal fistulae (RVF) are notoriously challenging to treat. Martius flap (MF) is a technique employed to manage RVF, among various others, with none being universally successful. We aimed to assess the outcomes of RVF managed with MF interposition. METHODS: A PRISMA-compliant meta-analysis searching for all studies specifically reporting on the outcomes of MF for RVF was performed. The primary objective was the mean success rate, whilst secondary objectives included complications and recurrence. The MedCalc software (version 20.118) was used to conduct proportional meta-analyses of data. Weighted mean values with 95% CI are presented and stratified according to aetiology where possible. RESULTS: Twelve non-randomized (11 retrospective, 1 prospective) studies, assessing 137 MF were included. The mean age of the study population was 42.4 (±15.7), years. There were 44 primary and 93 recurrent RVF. The weighted mean success rate for MF when performed for primary RVF was 91.4% (95% CI: 79.45-98.46; I2 = 32.1%; P = 0.183) and that for recurrent RVF was 77.5% (95% CI: 62.24-89.67; I2 = 58.1%; P = 0.008). The weighted mean complication rate was 29% (95% CI: 8.98-54.68; I2 = 85.4%; P < 0.0001) and the overall recurrence rate was 12.0% (95% CI: 5.03-21.93; I2 = 52.3%; P = 0.021). When purely radiotherapy-induced RVF were evaluated, the mean overall success rate was 94.6% (95% CI: 83.33-99.75; I2 = 0%; P = 0.350). CONCLUSIONS: MF interposition appears to be more effective for primary than recurrent RVF. However, the poor quality of the data limits definitive conclusions being drawn and demands further assessment with randomized studies.

A Cross-Sectional Study for the Spectrum of Clinical Diagnosis in Patients Presenting With Macrocytosis.

Objective The objectives of this study were to determine the frequency of the clinical spectrum of diseases in patients with macrocytosis and to summarize the diagnostic evaluation of patients found to have macrocytosis on laboratory testing. Background This was a cross-sectional study that took place at the Department of Medicine in Combined Military Hospital, Rawalpindi, Pakistan, from January to June 2023. Methodology One hundred and five patients with macrocytosis with mean corpuscular volume (MCV) values > 100 fL (80 to 100 fL) were inducted as per inclusion and exclusion criteria. Informed consent was obtained from all patients. Complete blood counts (CBC), peripheral blood film, serum vitamin B12 levels, serum folate levels, renal function tests (RFTs), liver function tests (LFTs), and thyroid function tests (TFTs) were performed during the assessment. Results The commonest cause of macrocytosis was vitamin B12 deficiency followed by folate deficiency, combined vitamin B12 and folate deficiency, and other causes were also found in a few cases. Conclusion Serum vitamin B12 and folate deficiency are the most common preventable causes of macrocytosis.

Tendril Anatomy: A Tool for Correct Identification among Cucurbitaceous Taxa.

This research examined the histological micro-structure of tendril vasculature in cucurbitaceous taxa. In this research, the tendril anatomy of 17 taxa of Cucurbitaceae categorized into seven genera, including Cucumis (five species), Cucurbita and Luffa (three species each), Citrullus and Momordica (two species each) while Lagenaria and Praecitrullus (one species each), collected from different areas of the Thal desert were examined via microscopic imaging to explore its taxonomic significance. Tendril transverse sections were cut with a Shandon Microtome to prepare slides. The distinctive characteristics of taxonomic value (qualitative and quantitative) include tendril and vascular bundle shape, variation in the number of vascular bundles, tendril diameter length, layers of sclerenchyma, and shape of collenchyma and epidermal cells. Tendril shapes observed are irregular, slightly oval-shaped, slightly C shaped, angular (4-angled, 6-angled, or polygonal), and star shaped. Quantitative measurements were taken to analyze the data statistically using SPSS software. Cucurbita pepo had a maximum tendril diameter length of 656.1 µm and a minimum in Momordica balsamina of 123.05 µm. The highest number of vascular bundles (12) were noticed in Luffa acutangula var.amara. Angular type was prominent in collenchyma, and irregular shape was dominant in sclerenchyma cells. A maximum of seven to nine sclerenchyma layers were present in Lagenaria siceraria and a minimum of two or three layers in Cucumis melo subsp. agrestis, Cucumis melo var. flexuosus, and Cucumis melo var.cantalupensis. Epidermis cells also show great variations with a rectangular shape being dominant. Statistical UPGMA dendrogram clustering of tendril vasculature traits shows that histological sections studied with microscopic techniques can be used to identify species and will play a vital role in future taxonomic and phylogenic linkages.

Neutrophil-to-Lymphocyte Ratio and Absolute Lymphocyte Count as Early Diagnostic Tools for Corona Virus Disease 2019.

Background and objectives In comparison to real-time polymerase chain reaction (RT-PCR) testing, blood-related parameters including absolute lymphocyte count (ALC) and neutrophil-to-lymphocyte ratio (NLR) carry an indeterminate potential in the assessment of corona virus disease 2019 (COVID-19). Our main objective was to assess their efficacy in timely identification of COVID-19 patients and to determine whether these biomarkers can be employed as an early diagnostic tool in patients presenting with symptoms suggestive of COVID-19. Methodology This cross-sectional study was conducted at the Emergency Department of a Tertiary Care Hospital in Rawalpindi, Pakistan from November 2020 to March 2021. Patients suspected to have COVID-19 on a clinical basis (fever, cough or shortness of breath) were selected by using convenience non-probability sampling. RT-PCR was used to diagnose COVID-19 after evaluating NLR and ALC of the sample population. An NLR = 3.5 and ALC < 1 x 103 cells/mm3 was considered as the cut-off value. Statistical analysis was conducted via SPSS 23.0 (IBM Corp., Armonk, NY). Chi-square and independent t-tests were used to correlate various data variables, while p-value <0.05 was considered significant. Results Out of the 172 subjects included in the study, the mean age was 40.6 ± 10.0 years, while 51% of individuals were males. Fever was found to be the most prevalent complaint (94%). Double RT-PCR testing showed that 51.2% of the population was RT-PCR positive, having a mean ALC of 1.4 ± 0.9 x 103/mm3, significantly lower than RT-PCR negative cases (p < 0.001). In addition, NLR was drastically elevated for RT-PCR-positive individuals (p < 0.001) while it also had a distinctly high specificity of 91.7% among COVID-19 patients. Additionally, NLR did not correlate with any of the baseline patient-related parameters (presenting complaint, age, and gender). Conclusion NLR and ALC are potentially efficacious measures for an early diagnosis of COVID-19, and can be possibly utilized for an early diagnosis of COVID-19 suspects.

An automated nuclei segmentation of leukocytes from microscopic digital images.

Leukemia is a life-threatening disease. So far diagnosing of leukemia is manually carried out by the Hematologists that is time-consuming and error-prone. The crucial problem is leukocytes' nuclei segmentation precisely. This paper presents a novel technique to solve the problem by applying statistical methods of Gaussian mixture model through expectation maximization for the basic and challenging step of leukocytes' nuclei segmentation. The proposed technique is being tested on a set of 365 images and the segmentation results are validated both qualitatively and quantitatively with current state-of-the-art methods on the basis of ground truth data (manually marked images by medical experts). The proposed technique is qualitatively compared with current state-of-the-art methods on the basis of ground truth data through visual inspection on four different grounds. Finally, the proposed technique quantitatively achieved an overall segmentation accuracy, sensitivity and precision of 92.8%, 93.5% and 98.16% respectively while an overall F-measure of 95.75%.

Correction: RCER: Reliable Cluster-based Energy-aware Routing protocol for heterogeneous Wireless Sensor Networks.

[This corrects the article DOI: 10.1371/journal.pone.0222009.].

RCER: Reliable Cluster-based Energy-aware Routing protocol for heterogeneous Wireless Sensor Networks.

Nowadays, because of the unpredictable nature of sensor nodes, propagating sensory data raises significant research challenges in Wireless Sensor Networks (WSNs). Recently, different cluster-based solutions are designed for the improvement of network stability and lifetime, however, most of the energy efficient solutions are developed for homogeneous networks, and use only a distance parameter for the data communication. Although, some existing solutions attempted to improve the selection of next-hop based on energy factor, nevertheless, such solutions are unstable and lack a reducing data delivery interruption in overloaded links. The aim of our proposed solution is to develop Reliable Cluster-based Energy-aware Routing (RCER) protocol for heterogeneous WSN, which lengthen network lifetime and decreases routing cost. Our proposed RCER protocol make use of heterogeneity nodes with respect to their energy and comprises of two main phases; firstly, the network field is parted in geographical clusters to make the network more energy-efficient and secondly; RCER attempts optimum routing for improving the next-hop selection by considering residual-energy, hop-count and weighted value of Round Trip Time (RTT) factors. Moreover, based on computing the measurement of wireless links and nodes status, RCER restore routing paths and provides network reliability with improved data delivery performance. Simulation results demonstrate significant development of RCER protocol against their competing solutions.

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear.

Malaria is a serious worldwide disease, caused by a bite of a female Anopheles mosquito. The parasite transferred into complex life round in which it is grown and reproduces into the human body. The detection and recognition of Plasmodium species are possible and efficient through a process called staining (Giemsa). The staining process slightly colorizes the red blood cells (RBCs) but highlights Plasmodium parasites, white blood cells and artifacts. Giemsa stains nuclei, chromatin in blue tone and RBCs in pink color. It has been reported in numerous studies that manual microscopy is not a trustworthy screening technique when performed by nonexperts. Malaria parasites host in RBCs when it enters the bloodstream. This paper presents segmentation of Plasmodium parasite from the thin blood smear points on region growing and dynamic convolution based filtering algorithm. After segmentation, malaria parasite classified into four Plasmodium species: Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, and Plasmodium malaria. The random forest and K-nearest neighbor are used for classification base on local binary pattern and hue saturation value features. The sensitivity for malaria parasitemia (MP) is 96.75% on training and testing of the proposed approach while specificity is 94.59%. Beside these, the comparisons of the two features are added to the proposed work for classification having sensitivity is 83.60% while having specificity is 94.90% through random forest classifier based on local binary pattern feature.

Cloud-based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images.

The advancement of computer- and internet-based technologies has transformed the nature of services in healthcare by using mobile devices in conjunction with cloud computing. The classical phenomenon of patient-doctor diagnostics is extended to a more robust advanced concept of E-health, where remote online/offline treatment and diagnostics can be performed. In this article, we propose a framework which incorporates a cloud-based decision support system for the detection and classification of malignant cells in breast cancer, while using breast cytology images. In the proposed approach, shape-based features are used for the detection of tumor cells. Furthermore, these features are used for the classification of cells into malignant and benign categories using Naive Bayesian and Artificial Neural Network. Moreover, an important phase addressed in the proposed framework is the grading of the affected cells, which could help in grade level necessary medical procedures for patients during the diagnostic process. For demonstrating the e effectiveness of the proposed approach, experiments are performed on real data sets comprising of patients data, which has been collected from the pathology department of Lady Reading Hospital of Pakistan. Moreover, a cross-validation technique has been performed for the evaluation of the classification accuracy, which shows performance accuracy of 98% as compared to physical methods used by a pathologist for the detection and classification of the malignant cell. Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.

An ensemble classification of exudates in color fundus images using an evolutionary algorithm based optimal features selection.

Atomic recognition of the Exudates (EXs), the major symbol of diabetic retinopathy is essential for automated retinal images analysis. In this article, we proposed a novel machine learning technique for early detection and classification of EXs in color fundus images. The major challenge observed in the classification technique is the selection of optimal features to reduce computational time and space complexity and to provide a high degree of classification accuracy. To address these challenges, this article proposed an evolutionary algorithm based solution for optimal feature selection, which accelerates the classification process and reduces computational complexity. Similarly, three well-known classifiers that is, Naïve Bayes classifier, Support Vector Machine, and Artificial Neural Network are used for the classification of EXs. Moreover, an ensemble-based classifier is used for the selection of best classifier on the basis of majority voting technique. Experiments are performed on three well-known benchmark datasets and a real dataset developed at local Hospital. It has been observed that the proposed technique achieved an accuracy of 98% in the detection and classification of EXs in color fundus images.

Plasmodium life cycle stage classification based quantification of malaria parasitaemia in thin blood smears.

Visual inspection for the quantification of malaria parasitaemiain (MP) and classification of life cycle stage are hard and time taking. Even though, automated techniques for the quantification of MP and their classification are reported in the literature. However, either reported techniques are imperfect or cannot deal with special issues such as anemia and hemoglobinopathies due to clumps of red blood cells (RBCs). The focus of the current work is to examine the thin blood smear microscopic images stained with Giemsa by digital image processing techniques, grading MP on independent factors (RBCs morphology) and classification of its life cycle stage. For the classification of the life cycle of malaria parasite the k-nearest neighbor, Naïve Bayes and multi-class support vector machine are employed for classification based on histograms of oriented gradients and local binary pattern features. The proposed methodology is based on inductive technique, segment malaria parasites through the adaptive machine learning techniques. The quantification accuracy of RBCs is enhanced; RBCs clumps are split by analysis of concavity regions for focal points. Further, classification of infected and non-infected RBCs has been made to grade MP precisely. The training and testing of the proposed approach on benchmark dataset with respect to ground truth data, yield 96.75% MP sensitivity and 94.59% specificity. Additionally, the proposed approach addresses the process with independent factors (RBCs morphology). Finally, it is an economical solution for MP grading in immense testing.

Classification of acute lymphoblastic leukemia using deep learning.

Acute Leukemia is a life-threatening disease common both in children and adults that can lead to death if left untreated. Acute Lymphoblastic Leukemia (ALL) spreads out in children's bodies rapidly and takes the life within a few weeks. To diagnose ALL, the hematologists perform blood and bone marrow examination. Manual blood testing techniques that have been used since long time are often slow and come out with the less accurate diagnosis. This work improves the diagnosis of ALL with a computer-aided system, which yields accurate result by using image processing and deep learning techniques. This research proposed a method for the classification of ALL into its subtypes and reactive bone marrow (normal) in stained bone marrow images. A robust segmentation and deep learning techniques with the convolutional neural network are used to train the model on the bone marrow images to achieve accurate classification results. Experimental results thus obtained and compared with the results of other classifiers Naïve Bayesian, KNN, and SVM. Experimental results reveal that the proposed method achieved 97.78% accuracy. The obtained results exhibit that the proposed approach could be used as a tool to diagnose Acute Lymphoblastic Leukemia and its sub-types that will definitely assist pathologists.

Microscopic malaria parasitemia diagnosis and grading on benchmark datasets.

Malaria parasitemia diagnosis and grading is hard and still far from perfection. Inaccurate diagnosis and grading has caused tremendous deaths rate particularly in young children worldwide. The current research deeply reviews automated malaria parasitemia diagnosis and grading in thin blood smear digital images through image analysis and computer vision based techniques. Actually, state-of-the-art reveals that current proposed practices present partially or morphology dependent solutions to the problem of computer vision based microscopy diagnosis of malaria parasitemia. Accordingly, a deep appraisal of the current practices is investigated, compared and analyzed on benchmark datasets. The open gaps are highlighted and the future directions are laid down for a complete automated microscopy diagnosis for malaria parasitemia based on those factors that have not been affected by other diseases. Moreover, a general computer vision framework to perform malaria parasitemia estimation/grading is constructed in universal directions. Finally, remaining problems are highlighted and possible directions are suggested. RESEARCH HIGHLIGHTS: The current research presents a microscopic malaria parasitemia diagnosis and grading of malaria in thin blood smear digital images through image analysis and computer vision based techniques. The open gaps are highlighted and future directions for a complete automated microscopy diagnosis of malaria parasitemia mentioned.

Rouleaux red blood cells splitting in microscopic thin blood smear images via local maxima, circles drawing, and mapping with original RBCs.

Splitting the rouleaux RBCs from single RBCs and its further subdivision is a challenging area in computer-assisted diagnosis of blood. This phenomenon is applied in complete blood count, anemia, leukemia, and malaria tests. Several automated techniques are reported in the state of art for this task but face either under or over splitting problems. The current research presents a novel approach to split Rouleaux red blood cells (chains of RBCs) precisely, which are frequently observed in the thin blood smear images. Accordingly, this research address the rouleaux splitting problem in a realistic, efficient and automated way by considering the distance transform and local maxima of the rouleaux RBCs. Rouleaux RBCs are splitted by taking their local maxima as the centres to draw circles by mid-point circle algorithm. The resulting circles are further mapped with single RBC in Rouleaux to preserve its original shape. The results of the proposed approach on standard data set are presented and analyzed statistically by achieving an average recall of 0.059, an average precision of 0.067 and F-measure 0.063 are achieved through ground truth with visual inspection.

Report: Nuclei segmentation of leukocytes in blood smear digital images.

The Leukocytes are differentiated from each other on the basis of their nuclei, demanded in many Medical studies, especially in all types of Leukemia by the Hematologists to note the disorder caused by specific type of Leukocyte. Leukemia is a life threatening disease. The work for diagnosing is manually carried out by the Hematologists involving much labor, time and human errors. The problems mentioned are easily addressed through computer vision techniques, but still accuracy and efficiency are demanded in terms of the basic and challenging step segmentation of Leukocyte's nuclei. The underlying study proposed better method in terms of accuracy and efficiency by designing a dynamic convolution filter for boosting low intensity values in the separated green channel of an RGB image and suppressing the high values in the same channel. The high values in the green channel become 255 (background) while the nuclei always have low values in the green channel and thus clearly appear as foreground. The proposed technique is tested on 365 images achieving an overall accuracy of 95.89%, while improving the efficiency by 10%. The proposed technique achieved its targets in a realistic way by improving the accuracy as well as the efficiency and both are highly required in the area.