An intelligent interval forecasting system based on fuzzy time series and error distribution characteristics for air quality index.

Due to the emergency environment pollution problems, it is imperative to understand the air quality and take effective measures for environmental governance. As a representative measure, the air quality index (AQI) is a single conceptual index value simplified by the concentrations of several routinely monitored air pollutants according to the proportion of various components in the air. With the gradual enhancement of awareness of environmental protection, air quality index forecasting is a key point of environment management. However, most of the traditional forecasting methods ignore the fuzziness of original data itself and the uncertainty of forecasting results which causes the unsatisfactory results. Thus, an innovative forecasting system combining data preprocessing technique, kernel fuzzy c-means (KFCM) clustering algorithm and fuzzy time series is successfully developed for air quality index forecasting. Concretely, the fuzzy time series that handle the fuzzy set is used for the main forecasting process. Then the complete ensemble empirical mode decomposition and KFCM are respectively developed for data denoising and interval partition. Furthermore, the interval forecasting method based on error distribution is developed to measure the forecasting uncertainty. Finally, the experimental simulation and evaluation system verify the great performance of proposed forecasting system and the promising applicability in a practical environment early warning system.

Effective prediction of human skin cancer using stacking based ensemble deep learning algorithm.

Automated diagnosis of cancer from skin lesion data has been the focus of numerous research. Despite that it can be challenging to interpret these images because of features like colour illumination changes, variation in the sizes and forms of the lesions. To tackle these problems, the proposed model develops an ensemble of deep learning techniques for skin cancer diagnosis. Initially, skin imaging data are collected and preprocessed using resizing and anisotropic diffusion to enhance the quality of the image. Preprocessed images are fed into the Fuzzy-C-Means clustering technique to segment the region of diseases. Stacking-based ensemble deep learning approach is used for classification and the LSTM acts as a meta-classifier. Deep Neural Network (DNN) and Convolutional Neural Network (CNN) are used as input for LSTM. This segmented images are utilized to be input into the CNN, and the local binary pattern (LBP) technique is employed to extract DNN features from the segments of the image. The output from these two classifiers will be fed into the LSTM Meta classifier. This LSTM classifies the input data and predicts the skin cancer disease. The proposed approach had a greater accuracy of 97%. Hence, the developed model accurately predicts skin cancer disease.

Unsupervised Learning-Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone.

OBJECTIVES: To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health. METHODS: This study presents an unsupervised learning approach for the automatic detection of first arrival time and estimation of ultrasonic velocity from axial transmission waveforms, which potentially indicates bone quality. The proposed method combines the ReliefF algorithm and fuzzy C-means clustering. It was first validated using an in vitro dataset measured from a Sawbones phantom. It was subsequently applied on in vivo signals collected from 40 infants, comprising 21 males and 19 females. The extracted neonatal ultrasonic velocity was subjected to statistical analysis to explore correlations with the infants' anthropometric features and biochemical indicators. RESULTS: The results of in vivo data analysis revealed significant correlations between the extracted ultrasonic velocity and the neonatal anthropometric measurements and biochemical markers. The velocity of first arrival signals showed good associations with body weight (ρ = 0.583, P value <.001), body length (ρ = 0.583, P value <.001), and gestational age (ρ = 0.557, P value <.001). CONCLUSION: These findings suggest that fuzzy C-means clustering is highly effective in extracting ultrasonic propagating velocity in bone and reliably applicable in in vivo measurement. This work is a preliminary study that holds promise in advancing the development of a standardized ultrasonic tool for assessing neonatal bone health. Such advancements are crucial in the accurate diagnosis of bone growth disorders.

Using Fuzzy C-Means Clustering to Determine First Arrival of Microseismic Recordings.

Accurate and automatic first-arrival picking is one of the most crucial steps in microseismic monitoring. We propose a method based on fuzzy c-means clustering (FCC) to accurately divide microseismic data into useful waveform and noise sections. The microseismic recordings' polarization linearity, variance, and energy are employed as inputs for the fuzzy clustering algorithm. The FCC produces a membership degree matrix that calculates the membership degree of each feature belonging to each cluster. The data section with the higher membership degree is identified as the useful waveform section, whose first point is determined as the first arrival. The extracted polarization linearity improves the classification performance of the fuzzy clustering algorithm, thereby enhancing the accuracy of first-arrival picking. Comparison tests using synthetic data with different signal-to-noise ratios (SNRs) demonstrate that the proposed method ensures that 94.3% of the first arrivals picked have an error within 2 ms when SNR = -5 dB, surpassing the residual U-Net, Akaike information criterion, and short/long time average ratio approaches. In addition, the proposed method achieves a picking accuracy of over 95% in the real dataset tests without requiring labelled data.

Fast Automatic Fuzzy C-Means Knitting Pattern Color-Separation Algorithm Based on Superpixels.

Patterns entered into knitting CAD have thousands or tens of thousands of different colors, which need to be merged by color-separation algorithms. However, for degraded patterns, the current color-separation algorithms cannot achieve the desired results, and the clustering quantity parameter needs to be managed manually. In this paper, we propose a fast and automatic FCM color-separation algorithm based on superpixels, which first uses the Real-ESRGAN blind super-resolution network to clarify the degraded patterns and obtain high-resolution images with clear boundaries. Then, it uses the improved MMGR-WT superpixel algorithm to pre-separate the high-resolution images and obtain superpixel images with smooth and accurate edges. Subsequently, the number of superpixel clusters is automatically calculated by the improved density peak clustering (DPC) algorithm. Finally, the superpixels are clustered using fast fuzzy c-means (FCM) based on a color histogram. The experimental results show that not only is the algorithm able to automatically determine the number of colors in the pattern and achieve the accurate color separation of degraded patterns, but it also has lower running time. The color-separation results for 30 degraded patterns show that the segmentation accuracy of the color-separation algorithm proposed in this paper reaches 95.78%.

Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds.

Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach. This study aims to explore the spatial analysis and diagnosis method of aquatic biological based on the combination of machine learning and statistical analysis, so as to reveal the spatial differentiation patterns and causes of changes of aquatic biological integrity in semi-arid regions. To this end, we have introduced an innovative approach that combines XGBoost-SHAP and Fuzzy C-means clustering (FCM), we successfully identified and diagnosed the spatial variations of aquatic biological integrity in the Wei River Basin (WRB). The study reveals significant spatial variations in species number, diversity, and aquatic biological integrity of phytoplankton, serving as a testament to the multifaceted responses of biological communities under the intricate tapestry of environmental gradients. Delving into the depths of the XGBoost-SHAP algorithm, we discerned that Annual average Temperature (AT) stands as the pivotal driver steering the spatial divergence of the Phytoplankton Integrity Index (P-IBI), casting a positive influence on P-IBI when AT is below 11.8 °C. The intricate interactions between hydrological variables (VF and RW) and AT, as well as between water quality parameters (WT, NO3-N, TP, COD) and AT, collectively sculpt the spatial distribution of P-IBI. The fusion of XGBoost-SHAP with FCM unveils pronounced north-south gradient disparities in aquatic biological integrity across the watershed, segmenting the region into four distinct zones. This establishes scientific boundary conditions for the conservation strategies and management practices of aquatic ecosystems in the region, and its flexibility is applicable to the analysis of spatial heterogeneity in other complex environmental contexts.

Inferring circadian gene regulatory relationships from gene expression data with a hybrid framework.

BACKGROUND: The central biological clock governs numerous facets of mammalian physiology, including sleep, metabolism, and immune system regulation. Understanding gene regulatory relationships is crucial for unravelling the mechanisms that underlie various cellular biological processes. While it is possible to infer circadian gene regulatory relationships from time-series gene expression data, relying solely on correlation-based inference may not provide sufficient information about causation. Moreover, gene expression data often have high dimensions but a limited number of observations, posing challenges in their analysis. METHODS: In this paper, we introduce a new hybrid framework, referred to as Circadian Gene Regulatory Framework (CGRF), to infer circadian gene regulatory relationships from gene expression data of rats. The framework addresses the challenges of high-dimensional data by combining the fuzzy C-means clustering algorithm with dynamic time warping distance. Through this approach, we efficiently identify the clusters of genes related to the target gene. To determine the significance of genes within a specific cluster, we employ the Wilcoxon signed-rank test. Subsequently, we use a dynamic vector autoregressive method to analyze the selected significant gene expression profiles and reveal directed causal regulatory relationships based on partial correlation. CONCLUSION: The proposed CGRF framework offers a comprehensive and efficient solution for understanding circadian gene regulation. Circadian gene regulatory relationships are inferred from the gene expression data of rats based on the Aanat target gene. The results show that genes Pde10a, Atp7b, Prok2, Per1, Rhobtb3 and Dclk1 stand out, which have been known to be essential for the regulation of circadian activity. The potential relationships between genes Tspan15, Eprs, Eml5 and Fsbp with a circadian rhythm need further experimental research.

Gluteal muscle fatty infiltration, fall risk, and mobility limitation in older women with urinary incontinence: a pilot study.

OBJECTIVE: Gluteal muscle quality influences risk of falling and mobility limitation. We sought (1) to compare gluteal muscle fatty infiltration (FI) between groups of older women with urinary incontinence (UI) at risk for falling (at-risk group) and not at risk for falling (not-at-risk group), and (2) to determine correlation of gluteal FI with Timed Up and Go (TUG) and Short Physical Performance Battery (SPPB) performance. MATERIALS AND METHODS: Prospective pilot study of gluteal FI on pelvis MRI for 19 women with UI, aged ≥ 70 years. A musculoskeletal radiologist selected axial T1-weighted MR images; then, two blinded medical student research assistants analyzed gluteal FI by quantitative fuzzy C-means segmentation. TUG and SPPB tests were performed. TUG ≥ 12 s defined participants as at risk for falling. Descriptive, correlation, and reliability analyses were performed. RESULTS: Mean age, 76.3 ± 4.8 years; no difference for age or body mass index (BMI) between the at-risk (n = 5) versus not-at-risk (n = 14) groups. SPPB score (p = 0.013) was lower for the at-risk group (6.4 ± 3.1) than for the not-at-risk group (10.2 ± 1.9). Fuzzy C-means FI-%-estimate differed between the at-risk group and the not-at-risk group for bilateral gluteus medius/minimus (33.2% ± 15.6% versus 19.5% ± 4.1%, p = 0.037) and bilateral gluteus maximus (33.6% ± 15.6% versus 19.7% ± 6.9%, p = 0.047). Fuzzy C-means FI-%-estimate for bilateral gluteus maximus had significant (p < 0.050) moderate correlation with age (rho = - 0.64), BMI (rho = 0.65), and TUG performance (rho = 0.52). Fuzzy C-means FI-%-estimates showed excellent inter-observer and intra-observer reliability (intraclass correlation coefficient, ≥ 0.892). CONCLUSION: Older women with UI at risk for falling have greater levels of gluteal FI and mobility limitation as compared to those not at risk for falling.

FFK: Fourier-Transform Fuzzy-c-means Kalman-Filter Based RSSI Filtering Mechanism for Indoor Positioning.

As indoor positioning has been widely utilized for many applications of the Internet of Things, the Received Signal Strength Indication (RSSI) fingerprint has become a common approach to distance estimation because of its simple and economical design. The combination of a Gaussian filter and a Kalman filter is a common way of establishing an RSSI fingerprint. However, the distributions of RSSI values can be arbitrary distributions instead of Gaussian distributions. Thus, we propose a Fouriertransform Fuzzyc-means Kalmanfilter (FFK) based RSSI filtering mechanism to establish a stable RSSI fingerprint value for distance estimation in indoor positioning. FFK is the first RSSI filtering mechanism adopting the Fourier transform to abstract stable RSSI values from the low-frequency domain. Fuzzy C-Means (FCM) can identify the major Line of Sight (LOS) cluster by its fuzzy membership design in the arbitrary RSSI distributions, and thus FCM becomes a better choice than the Gaussian filter for capturing LOS RSSI values. The Kalman filter summarizes the fluctuating LOS RSSI values as the stable latest RSSI value for the distance estimation. Experiment results from a realistic environment show that FFK achieves better distance estimation accuracy than the Gaussian filter, the Kalman filter, and their combination, which are used by the related works.

Indoor Human Action Recognition Based on Dual Kinect V2 and Improved Ensemble Learning Method.

Indoor human action recognition, essential across various applications, faces significant challenges such as orientation constraints and identification limitations, particularly in systems reliant on non-contact devices. Self-occlusions and non-line of sight (NLOS) situations are important representatives among them. To address these challenges, this paper presents a novel system utilizing dual Kinect V2, enhanced by an advanced Transmission Control Protocol (TCP) and sophisticated ensemble learning techniques, tailor-made to handle self-occlusions and NLOS situations. Our main works are as follows: (1) a data-adaptive adjustment mechanism, anchored on localization outcomes, to mitigate self-occlusion in dynamic orientations; (2) the adoption of sophisticated ensemble learning techniques, including a Chirp acoustic signal identification method, based on an optimized fuzzy c-means-AdaBoost algorithm, for improving positioning accuracy in NLOS contexts; and (3) an amalgamation of the Random Forest model and bat algorithm, providing innovative action identification strategies for intricate scenarios. We conduct extensive experiments, and our results show that the proposed system augments human action recognition precision by a substantial 30.25%, surpassing the benchmarks set by current state-of-the-art works.

Joint Learning of Correlation-Constrained Fuzzy Clustering and Discriminative Non-Negative Representation for Hyperspectral Band Selection.

Hyperspectral band selection plays an important role in overcoming the curse of dimensionality. Recently, clustering-based band selection methods have shown promise in the selection of informative and representative bands from hyperspectral images (HSIs). However, most existing clustering-based band selection methods involve the clustering of original HSIs, limiting their performance because of the high dimensionality of hyperspectral bands. To tackle this problem, a novel hyperspectral band selection method termed joint learning of correlation-constrained fuzzy clustering and discriminative non-negative representation for hyperspectral band selection (CFNR) is presented. In CFNR, graph regularized non-negative matrix factorization (GNMF) and constrained fuzzy C-means (FCM) are integrated into a unified model to perform clustering on the learned feature representation of bands rather than on the original high-dimensional data. Specifically, the proposed CFNR aims to learn the discriminative non-negative representation of each band for clustering by introducing GNMF into the model of the constrained FCM and making full use of the intrinsic manifold structure of HSIs. Moreover, based on the band correlation property of HSIs, a correlation constraint, which enforces the similarity of clustering results between neighboring bands, is imposed on the membership matrix of FCM in the CFNR model to obtain clustering results that meet the needs of band selection. The alternating direction multiplier method is adopted to solve the joint optimization model. Compared with existing methods, CFNR can obtain a more informative and representative band subset, thus can improve the reliability of hyperspectral image classifications. Experimental results on five real hyperspectral datasets demonstrate that CFNR can achieve superior performance compared with several state-of-the-art methods.

Design and Implementation of a Fuzzy Classifier for FDI Applied to Industrial Machinery.

In the present work, the design and the implementation of a Fault Detection and Isolation (FDI) system for an industrial machinery is proposed. The case study is represented by a multishaft centrifugal compressor used for the syngas manufacturing. The system has been conceived for the monitoring of the faults which may damage the multishaft centrifugal compressor: instrument single and multiple faults have been considered as well as process faults like fouling of the compressor stages and break of the thrust bearing. A new approach that combines Principal Component Analysis (PCA), Cluster Analysis and Pattern Recognition is developed. A novel procedure based on the statistical test ANOVA (ANalysis Of VAriance) is applied to determine the most suitable number of Principal Components (PCs). A key design issue of the proposed fault isolation scheme is the data Cluster Analysis performed to solve the practical issue of the complexity growth experienced when analyzing process faults, which typically involve many variables. In addition, an automatic online Pattern Recognition procedure for finding the most probable faults is proposed. Clustering procedure and Pattern Recognition are implemented within a Fuzzy Faults Classifier module. Experimental results on real plant data illustrate the validity of the approach. The main benefits produced by the FDI system concern the improvement of the maintenance operations, the enhancement of the reliability and availability of the compressor, the increase in the plant safety while achieving reduction in plant functioning costs.

UAV hyperspectral remote sensor images for mango plant disease and pest identification using MD-FCM and XCS-RBFNN.

To diminish disease transmission together with promoting effective management techniques, it is crucial to monitor plant health and detect pathogens earlier. The initial part in reducing losses sourced from plant diseases is to make an accurate and earlier identification. Thus, the usage of unmanned aerial vehicle (UAV) hyperspectral imaging (HSI) sensors for surveying and assessing crops, orchards, and forests has rapidly elevated over the last decade, particularly for the stress management like water, diseases, nutrition deficits, and pests. Using Minkowski Distance-based Fuzzy C Means (MD-FCM) clustering and Xavier initialization-adapted Cosine Similarity-induced Radial Bias Function Neural Network (XCS-RBFNN) techniques, a UAV HS imaging remote sensor for Spatial and Temporal Resolution (STR) of mango plant disease and pest identification is proposed in this scheme. Collecting the input UAV source (image or video) is eventuated initially along with the Region of Interest (ROI) calculated which is followed by preprocessing. Leaf segmentation is eventuated using Logistic U-net after preprocessing. Next, MD-FCM performs clustering to cluster the diseased leaves and pests individually. The disease and pest characteristics are then retrieved separately and classified further. The requisite features are then chosen from the retrieved features utilizing the Levy Flight Distribution-produced Butterfly Optimization Algorithm (LFD-BOA). Finally, the XCS-RBFNN classifier is utilized to categorize the various diseases together with pests found in the UAV input source using the chosen features. The proposed framework's experimental findings are then compared to some prevailing schemes, with the results revealing that the proposed work outperforms other benchmark techniques.

Fuzzy C-Means Clustering: A Review of Applications in Breast Cancer Detection.

This paper reviews the potential use of fuzzy c-means clustering (FCM) and explores modifications to the distance function and centroid initialization methods to enhance image segmentation. The application of interest in the paper is the segmentation of breast tumours in mammograms. Breast cancer is the second leading cause of cancer deaths in Canadian women. Early detection reduces treatment costs and offers a favourable prognosis for patients. Classical methods, like mammograms, rely on radiologists to detect cancerous tumours, which introduces the potential for human error in cancer detection. Classical methods are labour-intensive, and, hence, expensive in terms of healthcare resources. Recent research supplements classical methods with automated mammogram analysis. The basic FCM method relies upon the Euclidean distance, which is not optimal for measuring non-spherical structures. To address these limitations, we review the implementation of a Mahalanobis-distance-based FCM (FCM-M). The three objectives of the paper are: (1) review FCM, FCM-M, and three centroid initialization algorithms in the literature, (2) illustrate the effectiveness of these algorithms in image segmentation, and (3) develop a Python package with the optimized algorithms to upload onto GitHub. Image analysis of the algorithms shows that using one of the three centroid initialization algorithms enhances the performance of FCM. FCM-M produced higher clustering accuracy and outlined the tumour structure better than basic FCM.

A Repair Method for Missing Traffic Data Based on FCM, Optimized by the Twice Grid Optimization and Sparrow Search Algorithms.

Complete traffic sensor data is a significant prerequisite for analyzing the changing rules of traffic flow and formulating traffic control strategies. Nevertheless, the missing traffic data are common in practice. In this study, an improved Fuzzy C-Means algorithm is proposed to repair missing traffic data, and three different repair modes are established according to the correlation of time, space, and attribute value of traffic flow. First, a Twice Grid Optimization (TGO) algorithm is proposed to provide a reliable initial clustering center for the FCM algorithm. Then the Sparrow Search Algorithm (SSA) is used to optimize the fuzzy weighting index m and classification number k of the FCM algorithm. Finally, an experimental test of the traffic sensor data in Shunyi District, Beijing, is employed to verify the effectiveness of the TGO-SSA-FCM. Experimental results showed that the improved algorithm had a better performance than some traditional algorithms, and different data repair modes should be selected under different miss rate conditions.

Investigations on coronary artery plaque detection and subclassification using machine learning classifier.

Coronary artery diseases are one of the high-risk diseases, which occur due to the insufficient blood supply to the heart. The different types of plaques formed inside the artery leads to the blockage of the blood stream. Understanding the type of plaques along with the detection and classification of plaques supports in reducing the mortality of patients. The objective of this study is to present a novel clustering method of plaque segmentation followed by wavelet transform based feature extraction. The extracted features of all different kinds of calcified and sub calcified plaques are applied to first train and test three machine learning classifiers including support vector machine, random forest and decision tree classifiers. The bootstrap ensemble classifier then decides the best classification result through a voting method of three classifiers. A training dataset including 64 normal CTA images and 73 abnormal CTA images is used, while a testing dataset consists of 111 normal CTA images and 103 abnormal CTA images. The evaluation metrics shows better classification rate and accuracy of 97.7%. The Sensitivity and Specificity rates are 97.8% and 97.5%, respectively. As a result, our study results demonstrate the feasibility and advantages of developing and applying this new image processing and machine learning scheme to assist coronary artery plaque detection and classification.

A novel project portfolio selection framework towards organizational resilience: Robust Ordinal Priority Approach.

The COVID-19 pandemic has affected the world's economic condition significantly, and construction projects have faced many challenges and disruptions as well. This should be an alarm bell for project-oriented organizations to be prepared for such events and take necessary actions at the earliest time. In this regard, project-oriented organizations should establish their business based on the resilience concept, making them flexible in dealing with risks and decreasing the recovery time after disruptions. The current study proposes a practical conceptual framework for project-oriented organizations to select the most appropriate portfolio based on organizational resilience strategy. First, portfolios are identified, and the projects are clustered based on organizational resilience strategy using the Elbow and Fuzzy C-Means methods. The projects' scores are then determined employing the stakeholders' opinions and Robust Ordinal Priority Approach (OPA-R), which can handle the uncertainty of the input data. After that, each portfolio's score is determined using the obtained scores of the projects, and the best portfolio linked to the organizational resilience strategy is selected. The application of the proposed method to a project-oriented organization is examined, and its usage for the managers of project-oriented organizations is discussed in detail.

Increasing global supply chains' resilience after the COVID-19 pandemic: Empirical results from a Delphi study.

COVID-19 has revealed global supply chains' vulnerability and sparked debate about increasing supply chain resilience (SCRES). Previous SCRES research has primarily focused on near-term responses to large-scale disruptions, neglecting long-term resilience approaches. We address this research gap by presenting empirical evidence from a Delphi study. Based on the resource dependence theory, we developed 10 projections for 2025 on promising supply chain adaptations, which were assessed by 94 international supply chain experts from academia and industry. The results reveal that companies prioritize bridging over buffering approaches as long-term responses for increasing SCRES. Promising measures include increasing risk criteria importance in supplier selection, supply chain collaboration, and supply chain mapping. In contrast, experts ascribe less priority to safety stocks and coopetition. Moreover, we present a stakeholder analysis confirming one of the resource dependence theory's central propositions for the future of global supply chains: companies differently affected by externalities will choose different countermeasures.

[Detection of microaneurysms in fundus images based on improved YOLOv4 with SENet embedded].

Microaneurysm is the initial symptom of diabetic retinopathy. Eliminating this lesion can effectively prevent diabetic retinopathy in the early stage. However, due to the complex retinal structure and the different brightness and contrast of fundus image because of different factors such as patients, environment and acquisition equipment, the existing detection algorithms are difficult to achieve the accurate detection and location of the lesion. Therefore, an improved detection algorithm of you only look once (YOLO) v4 with Squeeze-and-Excitation networks (SENet) embedded was proposed. Firstly, an improved and fast fuzzy c-means clustering algorithm was used to optimize the anchor parameters of the target samples to improve the matching degree between the anchors and the feature graphs; Then, the SENet attention module was embedded in the backbone network to enhance the key information of the image and suppress the background information of the image, so as to improve the confidence of microaneurysms; In addition, an spatial pyramid pooling was added to the network neck to enhance the acceptance domain of the output characteristics of the backbone network, so as to help separate important context information; Finally, the model was verified on the Kaggle diabetic retinopathy dataset and compared with other methods. The experimental results showed that compared with other YOLOv4 network models with various structures, the improved YOLOv4 network model could significantly improve the automatic detection results such as F-score which increased by 12.68%; Compared with other network models and methods, the automatic detection accuracy of the improved YOLOv4 network model with SENet embedded was obviously better, and accurate positioning could be realized. Therefore, the proposed YOLOv4 algorithm with SENet embedded has better performance, and can accurately and effectively detect and locate microaneurysms in fundus images.

An automated liver segmentation in liver iron concentration map using fuzzy c-means clustering combined with anatomical landmark data.

BACKGROUND: To estimate median liver iron concentration (LIC) calculated from magnetic resonance imaging, excluded vessels of the liver parenchyma region were defined manually. Previous works proposed the automated method for excluding vessels from the liver region. However, only user-defined liver region remained a manual process. Therefore, this work aimed to develop an automated liver region segmentation technique to automate the whole process of median LIC calculation. METHODS: 553 MR examinations from 471 thalassemia major patients were used in this study. LIC maps (in mg/g dry weight) were calculated and used as the input of segmentation procedures. Anatomical landmark data were detected and used to restrict ROI. After that, the liver region was segmented using fuzzy c-means clustering and reduced segmentation errors by morphological processes. According to the clinical application, erosion with a suitable size of the structuring element was applied to reduce the segmented liver region to avoid uncertainty around the edge of the liver. The segmentation results were evaluated by comparing with manual segmentation performed by a board-certified radiologist. RESULTS: The proposed method was able to produce a good grade output in approximately 81% of all data. Approximately 11% of all data required an easy modification step. The rest of the output, approximately 8%, was an unsuccessful grade and required manual intervention by a user. For the evaluation matrices, percent dice similarity coefficient (%DSC) was in the range 86-92, percent Jaccard index (%JC) was 78-86, and Hausdorff distance (H) was 14-28 mm, respectively. In this study, percent false positive (%FP) and percent false negative (%FN) were applied to evaluate under- and over-segmentation that other evaluation matrices could not handle. The average of operation times could be reduced from 10 s per case using traditional method, to 1.5 s per case using our proposed method. CONCLUSION: The experimental results showed that the proposed method provided an effective automated liver segmentation technique, which can be applied clinically for automated median LIC calculation in thalassemia major patients.