Traditional Diagnosis Research Articles

Development of a Machine Learning Model for the Classification of Enterobius vermicularis Egg.

Enterobius vermicularis (pinworm) infections are a significant global health issue, affecting children predominantly in environments like schools and daycares. Traditional diagnosis using the scotch tape technique involves examining E. vermicularis eggs under a microscope. This method is time-consuming and depends heavily on the examiner's expertise. To improve this, convolutional neural networks (CNNs) have been used to automate the detection of pinworm eggs from microscopic images. In our study, we enhanced E. vermicularis egg detection using a CNN benchmarked against leading models. We digitized and augmented 40,000 images of E. vermicularis eggs (class 1) and artifacts (class 0) for comprehensive training, using an 80:20 training-validation and a five-fold cross-validation. The proposed CNN model showed limited initial performance but achieved 90.0% accuracy, precision, recall, and F1-score after data augmentation. It also demonstrated improved stability with an ROC-AUC metric increase from 0.77 to 0.97. Despite its smaller file size, our CNN model performed comparably to larger models. Notably, the Xception model achieved 99.0% accuracy, precision, recall, and F1-score. These findings highlight the effectiveness of data augmentation and advanced CNN architectures in improving diagnostic accuracy and efficiency for E. vermicularis infections.

A Linear time shrinking-SL(t)-ViT approach for brain tumor identification and categorization

Automated brain tumor detection and classification systems have gained popularity in recent years because traditional diagnosis procedures are time-consuming and costly in nature. Deep learning(DL) methods, specifically pre-trained convolutional neural networks (CNNs), have shown promising results in accurately and rapidly classifying brain tumors. However, the lack of diverse magnetic resonance imaging (MRI) datasets has hindered the ability of DL algorithms to generalize effectively. To address this issue, this paper proposes a DL model using generative adversarial networks (GANs) in conjunction with data augmentation strategies and a structural similarity loss function employed for generating annotated images. A novel DL model inspired from Vision Transformer, Shrinking Linear Time Vision Transformer (SL(t)-ViT) network model is proposed for disease data classification. The model underwent extensive evaluation across multiple datasets, employing standard performance metrics to assess its efficacy in brain tumor identification. The proposed model achieved remarkable testing accuracies of 0.995, 0.996, 0.9954, 0.998, and 0.997 for binary classification tasks across multiple datasets, and 0.986, 0.982, 0.985, and 0.993 for multi-class classification tasks. These results underscore the superior performance of our proposed model, showcasing its capability to outperform state-of-the-art techniques. Specifically, it demonstrated a substantial margin of improvement, ranging from 1-2% for binary classification and 9-10% for multi-class classification, solidifying its position as a leading approach in brain tumor identification. Results demonstrate the efficacy of the proposed model, outperforming other models. Overall, this study highlights the potential of SL(t)-ViT and GANs in improving the accuracy, resource consumption, and efficiency of brain tumor diagnosis.

Novel imbalanced fault diagnosis method based on generative adversarial networks with balancing serial CNN and Transformer (BCTGAN)

In industrial production engineering, machines cannot sustain faults for extended periods, resulting in limited fault data collection. This scarcity of data impedes the advancement of accurate fault diagnosis models, particularly in the context of small-sample, extremely imbalanced fault modeling, which poses a significant and challenging problem. In this paper, we introduce an innovative data augmentation approach for addressing the small-sample imbalanced problem: the balanced conditioning CNN (Convolutional Neural Networks)-Transformer architecture based Generative Adversarial Network (BCTGAN). Firstly, we employ a serial CNN and Transformer architecture as the foundation of the generator. Secondly, we introduce micro-enhancement and SMOTE (Synthetic Minority Oversampling Technique) enhancement of samples to facilitate model training. Micro-enhancement simply augments the sample size, while SMOTE enhancement enhances diversity. Together, these techniques balance the training process by introducing new loss function terms. Finally, outlier samples are mitigated through a designed multi-domain feature screening method. Despite the added time cost and computational complexity, results from three instances demonstrate that the proposed method holds superior diagnostic results with great potential compared to traditional and general GAN-based fault diagnosis methods. Our work provides theoretical and practical insights for modeling the field of small-sample blended disequilibrium fault diagnosis.

ViT-MAENB7: An innovative breast cancer diagnosis model from 3D mammograms using advanced segmentation and classification process

Tumors are an important health concern in modern times. Breast cancer is one of the most prevalent causes of death for women. Breast cancer is rapidly becoming the leading cause of mortality among women globally. Early detection of breast cancer allows patients to obtain appropriate therapy, increasing their probability of survival. The adoption of 3-Dimensional (3D) mammography for the medical identification of abnormalities in the breast reduced the number of deaths dramatically. Classification and accurate detection of lumps in the breast in 3D mammography is especially difficult due to factors such as inadequate contrast and normal fluctuations in tissue density. Several Computer-Aided Diagnosis (CAD) solutions are under development to help radiologists accurately classify abnormalities in the breast. In this paper, a breast cancer diagnosis model is implemented to detect breast cancer in cancer patients to prevent death rates. The 3D mammogram images are gathered from the internet. Then, the gathered images are given to the preprocessing phase. The preprocessing is done using a median filter and image scaling method. The purpose of the preprocessing phase is to enhance the quality of the images and remove any noise or artifacts that may interfere with the detection of abnormalities. The median filter helps to smooth out any irregularities in the images, while the image scaling method adjusts the size and resolution of the images for better analysis. Once the preprocessing is complete, the preprocessed image is given to the segmentation phase. The segmentation phase is crucial in medical image analysis as it helps to identify and separate different structures within the image, such as organs or tumors. This process involves dividing the preprocessed image into meaningful regions or segments based on intensity, color, texture, or other features. The segmentation process is done using Adaptive Thresholding with Region Growing Fusion Model (AT-RGFM)”. This model combines the advantages of both thresholding and region-growing techniques to accurately identify and delineate specific structures within the image. By utilizing AT-RGFM, the segmentation phase can effectively differentiate between different parts of the image, allowing for more precise analysis and diagnosis. It plays a vital role in the medical image analysis process, providing crucial insights for healthcare professionals. Here, the Modified Garter Snake Optimization Algorithm (MGSOA) is used to optimize the parameters. It helps to optimize parameters for accurately identifying and delineating specific structures within medical images and also helps healthcare professionals in providing more precise analysis and diagnosis, ultimately playing a vital role in the medical image analysis process. MGSOA enhances the segmentation phase by effectively differentiating between different parts of the image, leading to more accurate results. Then, the segmented image is fed into the detection phase. The tumor detection is performed by the Vision Transformer-based Multiscale Adaptive EfficientNetB7 (ViT-MAENB7) model. This model utilizes a combination of advanced algorithms and deep learning techniques to accurately identify and locate tumors within the segmented medical image. By incorporating a multiscale adaptive approach, the ViT-MAENB7 model can analyze the image at various levels of detail, improving the overall accuracy of tumor detection. This crucial step in the medical image analysis process allows healthcare professionals to make more informed decisions regarding patient treatment and care. Here, the created MGSOA algorithm is used to optimize the parameters for enhancing the performance of the model. The suggested breast cancer diagnosis performance is compared to conventional cancer diagnosis models and it showed high accuracy. The accuracy of the developed MGSOA-ViT-MAENB7 is 96.6 %, and others model like RNN, LSTM, EffNet, and ViT-MAENet given the accuracy to be 90.31 %, 92.79 %, 94.46 % and 94.75 %. The developed model's ability to analyze images at multiple scales, combined with the optimization provided by the MGSOA algorithm, results in a highly accurate and efficient system for detecting tumors in medical images. This cutting-edge technology not only improves the accuracy of diagnosis but also helps healthcare professionals tailor treatment plans to individual patients, ultimately leading to better outcomes. By outperforming traditional cancer diagnosis models, the proposed model is revolutionizing the field of medical imaging and setting a new standard for precision and effectiveness in healthcare.

Diagnosis of spine pseudoarthrosis based on the biomechanical properties of bone

BACKGROUND CONTEXTFailure to fuse following anterior cervical discectomy and fusion (ACDF) may result in symptomatic pseudoarthrosis. Traditional diagnosis involves computerized tomography to detect bridging bone and/or flexion-extension radiographs to assess whether segmental motion is above specific thresholds; however, there are currently no well-validated diagnostic tests. We propose a biomechanically rational approach to achieve a reliable diagnostic test for pseudoarthrosis. PURPOSEDevelop and test a biomechanically based approach to the diagnosis of pseudoarthrosis. STUDY DESIGNLiterature review, development of theory, re-analysis of a previously published study with surgical exploration as the gold-standard, and retrospective analysis of pooled studies to understand time to fusion. METHODSFully automated methods were used to measure disc space strains (change in disc space height divided by initial height). Measurement error combined with the reported failure strain of trabecular bone led to a proposed strain threshold for diagnosis of pseudoarthrosis following ACDF. We reanalyzed previously reported flexion-extension radiographs for asymptomatic volunteers to assess whether flexion-extension radiographs, in the absence of fusion surgery, can be expected to provide sufficient stress on motion segments to allow for reliable strain-based fusion assessment. The sensitivity and specificity of strain- and rotation-based pseudoarthrosis diagnosis were assessed by reanalysis of previously reported post-ACDF flexion-extension radiographs, where intraoperative fusion assessments were also available. Finally, we assessed changes in strain over time using 9,869 flexion-extension radiographs obtained 6 weeks to 84 months post-ACDF surgery from 1,369 patients. RESULTSThe estimated error in automated measurement of disc space strain from radiographs was approximately 3%, and the reported failure strain of bridging bone was less than 2.5%. On that basis, we propose a 5% strain threshold for pseudoarthrosis diagnosis. Reanalysis of a study in which intraoperative fusion assessments were available revealed 67% sensitivity and 82% specificity for strain-based diagnosis of pseudoarthrosis, which was comparable to rotation-based diagnosis. Analysis of post-ACDF flexion-extension radiographs revealed rapid strain reduction for up to 24 months, followed by a slower decrease for up to 84 months. When rotation is less than 2 degrees, the strain-based diagnosis differed from the rotation-based diagnosis in approximately 14% of the cases. CONCLUSIONSWe propose steps for standardizing diagnosis of pseudoarthrosis based on the failure strain of bone, measurement error, and retrospective data. These steps include obtaining high-quality flexion-extension studies, the application of proposed diagnostic thresholds, and the use of image stabilization for conclusive diagnosis, when motion is near thresholds. The necessity for an accurate diagnosis with minimal radiation exposure underscores the need for further optimization and standardization in diagnosing pseudoarthrosis following ACDF surgery. CLINICAL SIGNIFICANCEIn a symptomatic postspine fusion patient, it is important to diagnose or rule-out pseudoarthrosis. There are currently no well-validated diagnostic tests for this condition. Incorporating strain-based intervertebral motion analysis into the diagnosis could lead to a standardized and validated test for detecting spine pseudoarthrosis.

Reversible Fluorescent Probes for Dynamic Imaging of Liver Ischemia-Reperfusion Injury.

ConspectusHepatic ischemia-reperfusion injury (HIRI) is an inevitable complication of clinical surgeries such as liver resection or transplantation, often resulting in postoperative liver dysfunction, hepatic failure in up to 13% of postresection patients, and early graft failure in 11-18% of liver transplantation patients. HIRI involves a series of biochemical events triggered by abnormal alterations in multiple biomarkers, characterized by short lifespans, dynamic changes, subcellular regional distribution, and multicollaborative regulation. However, traditional diagnosis, including serology, imaging, and liver puncture biopsy, suffers from low sensitivity, poor resolution, and hysteresis, which hinder effective monitoring of HIRI markers. Thus, to address the unique properties of HIRI markers, there is a pressing demand for developing novel detection strategies that are highly selective, transiently responsive, dynamically reversible, subcellular organelle-targeted, and capable of simultaneous multicomponent analysis.Optical probe-based fluorescence imaging is a powerful tool for real-time monitoring of biomarkers with the advantages of high sensitivity, noninvasiveness, rapid analysis, and high-fidelity acquisition of spatiotemporal information on signaling molecules compared with conventional methods. Moreover, with the growing demand for continuous monitoring of biomarkers, probes with reversible detection features are receiving more and more attention. Importantly, reversible probes can not only monitor fluctuations in marker concentrations but also distinguish between transient bursts of markers during physiological events and long-term sustained increases in pathological marker levels. This can effectively avoid false-positive test results, and in addition, reversible probes can be reutilized with green and economical features. Therefore, our team has employed various effective methods to design reversible optical probes for HIRI. We proposed reversible recognition strategies based on specific reactions or interactions to detect dynamic changes in markers. Given the biomarkers' unique signaling in subcellular organelles and the synergistic regulatory properties of multiple markers for HIRI, bifunctional reversible detection strategies are exploited, including organelle-targeted reversible and multicomponent simultaneous detection. With these strategies, we have tailored a variety of high-fidelity fluorescent probes for a series of HIRI markers, including reactive oxygen/nitrogen species (O2•- and ONOO-), ATP, protein (Keap1), mitochondrial DNA, etc. Utilizing the probes, the in situ dynamic imaging detection of the HIRI markers was successfully achieved. While performing the precise examination of the earlier occurrence of HIRI disease and visualizing the real-time monitoring of the disease process, we have also further elucidated the HIRI-associated signaling pathways. It is envisioned that our summarized work will inspire the design of future reversible fluorescent probes and help to improve the clinical diagnosis and therapeutic efficiency of these diseases.

Use of Patterns of Service Utilization and Hierarchical Survival Analysis in Planning and Providing Care for Overdose Patients and Predicting the Time-to-Second Overdose

Individuals from a variety of backgrounds are affected by the opioid crisis. To provide optimal care for individuals at risk of opioid overdose and prevent subsequent overdoses, a more targeted response that goes beyond the traditional taxonomical diagnosis approach to care management needs to be adopted. In previous works, Graph Machine Learning and Natural Language Processing methods were used to model the products for planning and evaluating the treatment of patients with complex issues. This study proposes a methodology of partitioning patients in the opioid overdose cohort into various communities based on their patterns of service utilization (PSUs) across the continuum of care using graph community detection and applying survival analysis to predict time-to-second overdose for each of the communities. The results demonstrated that the overdose cohort is not homogeneous with respect to the determinants of risk. Moreover, the risk for subsequent overdose was quantified: there is a 51% higher chance of experiencing a second overdose for a high-risk community compared to a low-risk community. The proposed method can inform a more efficient treatment heterogeneity approach for a cohort made of diverse individuals, such as the opioid overdose cohort. It can also guide targeted support for patients at risk of subsequent overdoses.

Cross-domain diagnosis for polymer electrolyte membrane fuel cell based on digital twins and transfer learning network✰

Existing research on fault diagnosis for polymer electrolyte membrane fuel cells (PEMFC) has advanced significantly, yet performance is hindered by variations in data distributions and the requirement for extensive fault data. In this study, a cross-domain adaptive health diagnosis method for PEMFC is proposed, integrating the digital twin model and transfer convolutional diagnosis model. A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis method. To extract long-term time series features from the data, a temporal convolutional network (TCN) is proposed as a pre-trained diagnosis model for the source domain, with feature extraction layers that can be reused to the transfer learning network. It is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults, including pressure, drying, flow, and flooding faults, with 99.92 % accuracy, through the effective capture of the long-term dependencies in time series data. Finally, a domain adaptive transfer convolutional network (DATCN) is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant features. The results show that the DATCN model, tested on three different target domain devices with adversarial training using only 10 % normal data, can achieve an average accuracy of 98.5 % (30 % improved over traditional diagnosis models). This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices, significantly reducing the reliance on extensive fault data.

Comparing CNN-based Architectures for Dysgraphia Handwriting Classification Performance

Deep learning algorithms are increasingly being used to diagnose dysgraphia by concentrating on the issue of uneven handwriting characteristics, which is common among children in the early stage of basic learning of reading and writing skills. Convolutional Neural Network (CNN) is a deep learning model popular for classification tasks, including the dysgraphia detection process in assisting traditional diagnosis procedures. The CNN-based model is usually constructed by combining layers in the extraction network to capture the features of offline handwriting images before the classification network. However, concerns have been expressed regarding the limited study comparing the performance of the Directed Acyclic Graph (DAG) and Sequential Networks in handwriting-related studies in identifying dysgraphia. The proposed method was employed in this study to compare the two network structures utilized for feature extraction in classifying dysgraphia handwriting To eliminate this gap. Therefore, a new layer structure design in the Sequential and DAG networks was proposed to compare the performance of two feature extraction layers. The findings demonstrated that the DAG network outperforms the Sequential network with 1.75% higher accuracy in classification testing based on confusion matrix analysis. The study provides valuable insights into the efficiency of various network structures in recognizing inconsistencies identified in dysgraphia handwriting, underlining the need for additional research and improvement in this field. Subsequently, these findings highlight the necessity of deep learning approaches to advance dysgraphia identification and establish the framework for future research.

The assessment method of lip closure ability based on sEMG nonlinear onset detection algorithms.

To overcome the limitations of traditional diagnosis of orbicularis oris muscle function in mouth-breathing patients, this study aims to propose a surface electromyographic (sEMG) based method for reliable and accurate quantitative assessment of lip closure ability. A total of 21 volunteers (16 patients and 5 healthy subjects, aged 8-16) were included in the study. Three nonlinear onset detection algorithms- Teager-Kaiser Energy (TKE) operator, Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn)- were compared for their ability to identify lip closure in sEMG signals. Lip Closure EMG Activity Index (LCEAI) was proposed based on the action segments detected by the best performing algorithm for the quantitative assessment of lip closure. The results indicated that FuzzyEn had the highest lip closure identification rate at 93.78 %, the lowest average onset delay of 47.50 ms, the lowest average endpoint delay of 73.10 ms, and the minimal time error of 111.61 ms, exhibiting superior performance. The calculation results of the LCEAI closely corresponded with the actual degree of lip closure in patients. The lip closure ability assessment method proposed in this study can provide a quantitative basis for the diagnosis of mouth breathing.

AI-guided DVT diagnosis in primary care: protocol for cohort with qualitative assessment.

Deep vein thrombosis (DVT), a formation of blood clots within deep veins, mostly of the proximal lower limb, has an annual incidence of 1-2 per 1000. Patients who are affected by multiple chronic health conditions and who experience limited mobility are at high risk of developing DVT. Traditional DVT diagnosis involves probabilistic assessment in primary care, followed by specialised ultrasound scans (USS), mainly conducted in hospitals. The emergence of point-of-care ultrasound (POCUS), coupled with artificial intelligence (AI) applications, has the potential to expand primary care diagnostic capabilities. To assess the accuracy and acceptability of AI-guided POCUS for DVT diagnosis when performed by non-specialists in primary care. Diagnostic cross-sectional study coupled with a qualitative evaluation conducted at primary care DVT clinics. First, a diagnostic test accuracy (DTA) study will investigate the accuracy of AI-guided POCUS in 500 individuals with suspected DVT, performed by healthcare assistants (HCAs). The reference standard is the standard of care of USS conducted by sonographers. Second, after receiving both scans, participants will be invited to complete a patient satisfaction survey (PSS). Finally, semi-structured interviews with 20 participants and four HCAs, and three sonographers will explore the acceptability of AI-guided POCUS DVT diagnosis. This study will rigorously evaluate the accuracy and acceptability of AI-guided POCUS DVT diagnosis conducted by non-specialists in primary care.

Bearing fault diagnosis method based on recurrence plot and improved EfficientNetV2-S

The non-linear and non-stationary characteristics of vibration signals in rolling bearings make it difficult to accurately extract fault features. In addition, traditional fault diagnosis methods cannot fully explore the correlation characteristics between time-series of fault signals. To address the aforementioned issues, this paper introduces a recurrence plot (RP) coding technique into the field of fault diagnosis and proposes a bearing fault diagnosis method based on RP and the improved EfficientNetV2-S. Firstly, the method uses the RP coding technique to convert one-dimensional vibration signals into two-dimensional time-frequency images as inputs to the neural network. Then, the number of layers in the EfficientNetV2-S network is optimised by a non-linear attenuation strategy to reduce network parameters and improve the recognition speed. Finally, the attention mechanism is modified and the variable load dataset is constructed for training to improve the feature extraction ability and generalisation performance of the model. To verify the effectiveness of the proposed method, experiments are conducted based on the bearing datasets provided by Case Western Reserve University (CWRU). The experimental results show that the bearing fault diagnosis method based on RP and the improved EfficientNetV2-S cannot only realise accurate identification of bearing faults but also accurately identify the degree of bearing fault with an accuracy of 99.85%.

Bushing fault diagnosis based on SVM and the improved sparrow search algorithm

Abstract In order to address the issue of low precision in traditional bushing fault diagnosis, a bushing fault diagnosis method based on an improved sparrow search algorithm (ISSA) and support vector machine (SVM) is proposed in this paper. Firstly, the bushing vibration signals are extracted by wavelet packet, and the feature vectors are used as inputs for the SVM. In view of the impact of support vector machine parameters on the model, a sparrow search algorithm is proposed for intelligent optimization. To prevent reaching a local optimum, adaptive inertia weight is added based on the original approach. The final bushing fault diagnosis model is established by training. Comparison experiments with three fault diagnosis models, SSA-SVM, PSO-SVM, and SVM, found that the proposed method achieves complete diagnosis in a shorter time, and the diagnostic accuracy rate is 96.5%, which verifies the feasibility and effectiveness of the model.

A real-world study of the differences in Traditional Chinese Medicine diagnosis and treatment rules for coronavirus disease 2019 between Northern and Southern China.

To explore the differences in Traditional Chinese Medicine (TCM) diagnosis and treatment rules for coronavirus disease 2019 (COVID-19) between Northern and Southern China based on the real-world data from 982 COVID-19 patients. All consecutive cases of COVID-19 admitted to the TCM department of designated COVID-19 hospitals in eight provinces and cities were retrospectively analyzed. Patients were divided into a Northern and a Southern group according to the location of the admitting hospital. The symptoms, syndrome elements, syndrome distribution and herbal treatments were analyzed. The core prescriptions were extracted using the multiscale backbone-based network comparison algorithm (msbNC). The distribution of syndrome elements showed that dampness was common in Northern and Southern China, wind and heat were more often present in the South, while fire toxin and spleen deficiency were more often encountered in the North. The distribution of syndromes showed that the South was dominated by heat dampness accumulating in the lung (55.69%), while the North was dominated by dampness-toxin stagnating in the lung (44.90%).The results of core prescription mining showed that dispelling dampness, dispersing wind, clearing heat and strengthening spleen were the common treatment methods in Northern and Southern China. For mild cases, Jinyinhua (Flos Lonicerae) and Lianqiao (Fructus Forsythiae Suspensae) were often used in the South to clear heat and relieve exterior symptoms, while Chaihu (Radix Bupleuri Chinensis) and Huangqin (Radix Scutellariae Baicalensis) were often used in the North to relieve muscles by expelling heat. For moderate cases, Chaihu (Radix Bupleuri Chinensis), Qinghao (Herba Artemisiae Annuae), and Shigao (Gypsum Fibrosum) were often used to clear heat of Tri-jiao Channel and stomach in the South, while Fuling (Poria), Chenpi (Pericarpium Citri Reticulatae), and Dangshen (Radix Codonopsis) were often used to invigorate spleen and remove dampness in the North. For severe cases, spleen invigoration and dampness removal as well as relaxing the bowels and discharging heat were often used in the North. There were certain North-South differences in terms of symptoms, syndrome elements and syndrome distribution of COVID-19, as well as differences in core prescriptions during different periods of the disease. The regional differences in the rules of TCM diagnosis and treatment for COVID-19 should be further considered in the process of optimization and revision of relevant treatment guidance.

Development and modification Sobel edge detection in tuberculosis X-ray images

Tuberculosis (TB), a major global health threat caused by mycobacterium tuberculosis, claims lives across all age groups, underscoring the urgent need for accurate diagnostic methods. Traditional TB diagnosis using X-ray images faces challenges in detection accuracy, highlighting a critical problem in medical imaging. Addressing this, our study investigates the use of image processing techniques-specifically, a dataset of 112 TB X-ray images-employing pre-processing, segmentation, edge detection, and feature extraction methods. Central to our method is the adoption of a modified Sobel edge detection technique, named modification and extended magnitude gradient (MEMG), designed to enhance TB identification from X-ray images. The effectiveness of MEMG is rigorously evaluated against the gray-level co-occurrence matrix (GLCM) parameters, contrast, and correlation, where it demonstrably surpasses the standard Sobel detection, amplifying the contrast value by over 50% and achieving a correlation value nearing 1. Consequently, the MEMG method significantly improves the clarity and detail of TB-related anomalies in X-ray images, facilitating more precise TB detection. This study concludes that leveraging the MEMG technique in TB diagnosis presents a substantial advancement over conventional methods, promising a more reliable tool for combating this global health menace.

An envelope statistic features and MFO-SVM fusion method for tidal stream turbine blades impact fault diagnosis

Abstract The fault diagnosis of a Tidal Stream Turbine (TST) blade impact fault benefits its stable operation. However, when the stream velocity changes, it is hard to distinguish between different fault severities directly by observing the changes in the signal feature. To address this problem, this paper proposes a blade impact fault diagnosis method based on envelope statistical features and MFO-SVM. The method is divided into two parts. In the first part, the Teager-Kaiser energy operator (TKEO) and sliding window technique are introduced to extract the envelope statistic features of the current signal, and then the local outlier factor (LOF) values of fault sample points are calculated to form a new set of feature samples; in the second part, the fault feature samples are input into the support vector machine (SVM) optimized by moth fame optimization (MFO) for fault diagnosis. The experimental results show that the proposed method is more accurate than traditional fault diagnosis methods.

IScan: Detection of Colorectal Cancer from CT Scan Images Using Deep Learning

Colorectal cancer, a highly lethal form of cancer, can be treated effectively if detected early. However, the current diagnosis process involves a time-consuming and manual review of CT scans to identify cancerous regions and behavior, leading to resource consumption, subjectivity, and dependency on manual assessment. We propose a 3-phase deep neural system for automated colorectal cancer detection using CT scan images to address these challenges. It includes a SegNet network to identify tumor locations, an InceptionResNet V2 network to classify tumors as benign or malignant, and an analysis of tumor area cum perimeter to predict the cancer stage. The proposed model offers a fully automated solution by combining these functionalities under a single umbrella. In real-life CT scans from 37 patients, the proposed model achieved 95.8% ROI segmentation accuracy, a dice coefficient of 0.6214, 69.75% IoU score, and 95.83% tumor classification accuracy. The unique approach using Radial Length (RL) and Circularity (C) parameters predicted the T-stage with close to 85% accuracy. Based on these outcomes, the proposed system establishes itself as a reliable and suitable alternative to traditional cancer diagnosis techniques by leveraging the power of automation, deep learning, and innovative parameter analysis.

Cervical Cancer Tissue Analysis Using Photothermal Midinfrared Spectroscopic Imaging.

Hyperspectral photothermal mid-infrared spectroscopic imaging (HP-MIRSI) is an emerging technology with promising applications in cervical cancer diagnosis and quantitative, label-free histopathology. This study pioneers the application of HP-MIRSI to the evaluation of clinical cervical cancer tissues, achieving excellent tissue type segmentation accuracy of over 95%. This achievement stems from an integrated approach of optimized data acquisition, computational data reconstruction, and the application of machine learning algorithms. The results are statistically robust, drawing from tissue samples of 98 cervical cancer patients and incorporating over 40 million data points. Traditional cervical cancer diagnosis methods entail biopsy, staining, and visual evaluation by a pathologist. This process is qualitative, subject to variations in staining and subjective interpretations, and requires extensive tissue processing, making it costly and time-consuming. In contrast, our proposed alternative can produce images comparable to those from histological analyses without the need for staining or complex sample preparation. This label-free, quantitative method utilizes biochemical data from HP-MIRSI and employs machine-learning algorithms for the rapid and precise segmentation of cervical tissue subtypes. This approach can potentially transform histopathological analysis by offering a more accurate and label-free alternative to conventional diagnostic processes.

Exploring the lived experiences of women with infertility using traditional healthcare services in Harare urban, Zimbabwe.

Infertility has a significant impact on the lives of women. Therefore, affected women often consider the treatment options available to deal with their condition, including traditional healthcare services (THS). The aim of this phenomenological study was to explore the lived experiences of women with infertility problems who sought help from traditional health practitioners in Harare, Zimbabwe. Data from interviews with five women with infertility was explicated using a simplified version of Hycner (1985) five step explication process. Two major themes and eight sub themes emerged from the findings. The major themes were traditional diagnosis experiences and traditional treatment experiences. Consultation and divination were the diagnosis methods experienced by the women with infertility. The THS offered comprehensive management of infertility through couples therapy, as well as pre- and post-natal therapies, which include lifestyle counselling. The findings also showed that women with infertility commonly receive concurrent treatment, including both allopathic and traditional medicine. This presents an opportunity to explore the convergence of traditional and allopathic approaches in the management of infertility in women.

VoiceAD: A multilingual speech based classifier for early detection of Alzheimers disease

Alzheimer's Disease (AD) causes memory decline beyond normal aging and is the most common cause of dementia. Early detection and intervention can delay the progression of AD and alleviate symptoms. However, high expense, insensitivity, and inconvenience are among various deterrents to traditional diagnosis methods. Dementia impacts more than 55 million people globally, with this number set to double every 20 years. Much of this increase will be in low- and middle-income countries which often lack the resources for early diagnosis. Recently speech has emerged as an effective digital biomarker for AD detection. However, most studies in this area are limited to English speech due to lack of suitable data in other languages. This study aims to construct a speech-based classifier (VoiceAD) that can be trained on English data and still maintain its performance on other languages. Acoustic features were extracted from English, Spanish, and Greek data from DementiaBank with the eGeMAPS v2.0 feature set. Subsequently, an Extra Trees model was employed for classification. VoiceADs performance for English and Greek surpassed that of the baseline model and model incorporating only demographic data while the Spanish performance was lower due to task effect. In addition, VoiceADs satisfactory generalizability and interpretability positions it as a promising candidate for clinical applications. It can be implemented as a free screening tool such as a mobile application to monitor AD risk. VoiceADs crosslingual applicability can its enable worldwide deployment, especially in settings where traditional methods of diagnosis are less attainable.