Jaccard Index Research Articles

Unified Generative Data Augmentation for Efficient Solar Panel Soiling Localization

As the usage of solar power generation increases, it has become essential to predict power generation accurately. Among the various factors that affect solar power generation, soiling on the panel surface drastically reduces solar power generation. Therefore, accurately identifying the area of soiling on the panel surface helps predict solar power generation. However, most existing studies classify the presence or absence of soiling on the panel or the type of soiling. Additionally, current datasets used for training these models, such as the Solar Panel Soiling Image (SPSI) dataset, suffer from limitations, including a lack of diversity in panel types and a small number of unique soiling shapes. To address these issues, we propose three novel data augmentation techniques—Naïve, Realistic, and Translucent—that generate diverse solar panel images with various soiling patterns. Using Pix2Pix and Copy-Paste methods, we created three corresponding datasets to address the imbalances in the existing SPSI dataset. We trained the DeepLabV3+ model for soiling localization using both the original SPSI dataset and our augmented datasets. Experimental evaluations on real-world solar panels installed at Chungbuk National University demonstrated that models trained on our proposed datasets significantly outperform those trained on SPSI data, with improvements in the Jaccard Index of 3.3%, 2.4%, and 14.6% for the Naïve, Realistic, and Translucent datasets, respectively. These results highlight the effectiveness of our data augmentation techniques for improving soiling localization in solar panels.

Identifying the factors influencing the development of bilateral investment treaties with health safeguards: a Machine Learning-based link prediction approach

A network analysis approach, complemented by machine learning (ML) techniques, is applied to analyse the factors influencing Bilateral Investment Treaties (BITs) at the country level. Using the Electronic Database of Investment Treaties, BITs with health safeguards from 167 countries were charted, resulting in 534 connections with countries as nodes and their BITs as edges. Network analysis found that, on average, a country established BITs with six other nations. Additionally, we used node embedding techniques to generate features from the network, such as the Jaccard coefficient, resource allocation, and Adamic Adar for downstream link prediction. This study employed five tree-based ML models to predict future BIT formations with health inclusion. The eXtreme Gradient Boosting model proved to be superior, achieving a 64.02% accuracy rate. Notably, the Common Neighbor centrality feature and the Capital Account Balance Ratio emerged as influential factors in creating new BITs with health inclusions. Beyond economic considerations, our study highlighted a vital intersection: the nexus between BITs, economic growth, and public health policies. In essence, this research underscores the importance of safeguarding public health in BITs and showcases the potential of ML in understanding the intricacies of international treaties.

DEFECT CHARACTERIZATION OF METALLIC MATERIALS USING SELF-PARAMETERIZED DENSITY-BASED CLUSTERING AND COMPUTATIONAL INTELLIGENCE TECHNIQUES

Rapid growth of manufacturing industries is propelled by transformative technologies such as machine intelligence, autonomous computing and non-destructive testing (NDT). During the manufacturing of wrought products, there is no guarantee that the final product is 100-% flawless. Thus, all final products are subjected to quality checking to identify and eliminate defective products. In industries, most of internal defects are identified using NDT techniques, which fail to precisely characterize the defects. In this paper a novel algorithm, called Self-Parameterized Density-Based Clustering (SPDBC), is proposed for defect characterization. The proposed clustering method uses spatial parameters to identify the size and position of defects by filtering out the noise and other data that correspond to the non-defect area. Using these filtered data, computational intelligence techniques are employed to predict the defect type. SPDBC achieved Jaccard indices of 97.02 % and 98.78 % for identifying the defect size and position, respectively. Gradient boosting regression trees (GBRT) achieved a maximum accuracy of 97.44 % in predicting the defect type. As a result, the proposed approach can assist NDT experts in various sectors to differentiate between problem severities faster and replace defective parts before any major breakdown occurs.

Advances and clinical Implications of neuroanatomical biomarkers in schizophrenia by optimizing brain tissue segmentation

Schizophrenia (SZ) is a complex mental disorder marked by structural abnormalities in brain regions such as gray matter (GM), white matter (WM), ventricles and the cerebellum. Accurate segmentation of these brain subregions is crucial for diagnosing SZ, but traditional methods often struggle with the complexity of the disorder. To address this, the study employs advanced optimization techniques, specifically Cuckoo Search (CS), Grey Wolf Optimization (GWO) and Improved Grey Wolf Optimization (IGWO), to segment regions extracted from T1-weighted MR images of SZ patients and HC/NC. The IGWO method consistently outperforms others, achieving Structural Similarity Index Measures (SSIM) of 0.98 for ventricles and 0.96 for the cerebellum, with segmentation accuracies of 99% for both regions. Overlapping measures also reflect the effectiveness of proposed method, with DICE coefficients of 0.89 (SZ) and 0.94 (healthy controls) for ventricles and JACCARD indices of 0.80 and 0.95, respectively. For the cerebellum, DICE coefficients were 0.89 (SZ) and 0.93 (healthy controls), with JACCARD indices of 0.79 and 0.88. Subsequent classification using the ResNet50 deep learning model achieved high accuracy rates, particularly for the cerebellum (94%) and ventricles (93%). Performance metrics including recall, specificity, precision and F1-score further validate the robustness of the proposed method. These results highlight the significant role of the cerebellum and ventricles in SZ progression and offer an enhanced approach to improving diagnostic accuracy.

Hybrid deep learning-based skin cancer classification with RPO-SegNet for skin lesion segmentation

ABSTRACT Skin melanin lesions are typically identified as tiny patches on the skin, which are impacted by melanocyte cell overgrowth. The number of people with skin cancer is increasing worldwide. Accurate and timely skin cancer identification is critical to reduce the mortality rates. An incorrect diagnosis can be fatal to the patient. To tackle these issues, this article proposes the Recurrent Prototypical Object Segmentation Network (RPO-SegNet) for the segmentation of skin lesions and a hybrid Deep Learning (DL) – based skin cancer classification. The RPO-SegNet is formed by integrating the Recurrent Prototypical Networks (RP-Net), and Object Segmentation Networks (O-SegNet). At first, the input image is taken from a database and forwarded to image pre-processing. Then, the segmentation of skin lesions is accomplished using the proposed RPO-SegNet. After the segmentation, feature extraction is accomplished. Finally, skin cancer classification and detection are accomplished by employing the Fuzzy-based Shepard Convolutional Maxout Network (FSCMN) by combining the Deep Maxout Network (DMN), and Shepard Convolutional Neural Network (ShCNN). The established RPO-SegNet+FSCMN attained improved accuracy, True Negative Rate (TNR), True Positive Rate (TPR), dice coefficient, Jaccard coefficient, and segmentation analysis of 91.985%, 92.735%, 93.485%, 90.902%, 90.164%, and 91.734%.

Many-Objective Jaccard-Based Evolutionary Feature Selection for High-Dimensional Imbalanced Data Classification.

Filters and wrappers represent two mainstream approaches to feature selection (FS). Although evolutionary wrapper-based FS outperforms filters in addressing real-world classification problems, extending these methods to high-dimensional, many-objective optimization problems with imbalanced data poses substantial challenges. Overcoming computational costs and identifying suitable performance metrics are vital for navigating search operation complexities. Here, we propose using the Jaccard similarity (JS) in a set-based evolutionary many-objective (JSEMO) FS search, addressing both evolutionary FS and imbalanced classifier choice concurrently. This study highlights the mutual influence between these aspects, impacting overall algorithm performance. JSEMO integrates JS into population initialization, reproduction, and elitism steps, enhancing diversity and avoiding duplicate solutions. The set-based variation operator utilizes intersection and union operators for compatibility with binary coding. We also introduce a double-weighted KNN (KNN2W) classifier with four supportive objectives as a many-objective FS problem to handle imbalanced distributions. Compared with 20 methods on 15 benchmark problems, JSEMO produces distinct optimal features, significantly improving overall accuracy, balance accuracy, and g-mean metrics with comparable feature set size and computational cost. The ablation study underscores the positive impact of all JSEMO components, highlighting the set-based variation operation with JS and KNN2W with relevant evaluation metrics as the most influential aspects.

Deep Learning for CAD Prediction: X-ray Angiography Insights

This study presents a deep learning-based approach to improve the prediction of coronary artery disease (CAD) using X-ray angiography images. The primary objective is to achieve accurate and automated CAD identification by employing a convolutional neural network (CNN) model. The methodology involves preprocessing the dataset through normalization and augmentation techniques and utilizes a U-Net architecture for precise detection of coronary stenosis. To ensure robustness and generalizability, hyperparameter tuning and dropout regularisation are applied during model training. The proposed model achieves high performance, with an average Dice coefficient of 0.57 and a Jaccard Index of 0.47 on a held-out test set, indicating its effectiveness in segmenting coronary artery stenosis. These findings support the integration of deep learning methods into clinical workflows for enhanced CAD diagnosis and early intervention.

Species composition, diversity and foraging guilds of avifauna in two freshwater lakes of Mysore district, Karnataka, India

Abstract We conducted the present study in two freshwater lakes in the Mysore district of Karnataka, India, from December 2023 to May 2024, with the aim of evaluating the diversity, relative abundance, and feeding guilds of avifauna in Giribetta and Hadinaru lakes. The “point count” method was used to evaluate the species composition of birds. A total of 5,790 individuals, representing 106 species from 18 orders and 48 families, were recorded. Four species were in the near-threatened category: Painted Stork (Mycteria leucocephala), Black-headed Ibis (Threskiornis melanocephalus), Oriental Darter (Anhinga melanogaster), and Spot-billed Pelican (Pelecanus philippensis), while the others were of least concern. In terms of species richness, Giribetta Lake had 87 species belonging to 18 orders and 42 families, as compared to Hadinaru Lake, which had 86 species from 16 orders and 40 families. The two areas had similar species composition, with a Sorenson index of 76.3% and a Jaccard index of 61.7%, which indicates that the species diversity is fairly similar across the habitats. Based on the diet and foraging habitat, the recorded bird species were classified into eight major feeding guilds, which revealed a higher abundance of carnivorous and insectivorous birds, followed by omnivorous, granivorous, frugivorous, nectarivorous, herbivorous, and mixed guild types. It is expected that this study will provide a preliminary database for the waterbirds of this area, useful for further research and assessment.

High-throughput mesoscopic optical imaging data processing and parsing using differential-guided filtered neural networks

High-throughput mesoscopic optical imaging technology has tremendously boosted the efficiency of procuring massive mesoscopic datasets from mouse brains. Constrained by the imaging field of view, the image strips obtained by such technologies typically require further processing, such as cross-sectional stitching, artifact removal, and signal area cropping, to meet the requirements of subsequent analyse. However, obtaining a batch of raw array mouse brain data at a resolution of 0.65×0.65×3μm3 can reach 220TB, and the cropping of the outer contour areas in the disjointed processing still relies on manual visual observation, which consumes substantial computational resources and labor costs. In this paper, we design an efficient deep differential guided filtering module (DDGF) by fusing multi-scale iterative differential guided filtering with deep learning, which effectively refines image details while mitigating background noise. Subsequently, by amalgamating DDGF with deep learning network, we propose a lightweight deep differential guided filtering segmentation network (DDGF-SegNet), which demonstrates robust performance on our dataset, achieving Dice of 0.92, Precision of 0.98, Recall of 0.91, and Jaccard index of 0.86. Building on the segmentation, we utilize connectivity analysis for ascertaining three-dimensional spatial orientation of each brain within the array. Furthermore, we streamline the entire processing workflow by developing an automated pipeline optimized for cluster-based message passing interface(MPI) parallel computation, which reduces the processing time for a mouse brain dataset to a mere 1.1 h, enhancing manual efficiency by 25 times and overall data processing efficiency by 2.4 times, paving the way for enhancing the efficiency of big data processing and parsing for high-throughput mesoscopic optical imaging techniques.

Key Node Identification Method Based on Multilayer Neighbor Node Gravity and Information Entropy

In the field of complex network analysis, accurately identifying key nodes is crucial for understanding and controlling information propagation. Although several local centrality methods have been proposed, their accuracy may be compromised if interactions between nodes and their neighbors are not fully considered. To address this issue, this paper proposes a key node identification method based on multilayer neighbor node gravity and information entropy (MNNGE). The method works as follows: First, the relative gravity of the nodes is calculated based on their weights. Second, the direct gravity of the nodes is calculated by considering the attributes of neighboring nodes, thus capturing interactions within local triangular structures. Finally, the centrality of the nodes is obtained by aggregating the relative and direct gravity of multilayer neighbor nodes using information entropy. To validate the effectiveness of the MNNGE method, we conducted experiments on various real-world network datasets, using evaluation metrics such as the susceptible-infected-recovered (SIR) model, Kendall τ correlation coefficient, Jaccard similarity coefficient, monotonicity, and complementary cumulative distribution function. Our results demonstrate that MNNGE can identify key nodes more accurately than other methods, without requiring parameter settings, and is suitable for large-scale complex networks.

Enhanced Conditional GAN for High-Quality Synthetic Tabular Data Generation in Mobile-Based Cardiovascular Healthcare.

The generation of synthetic tabular data has emerged as a critical task in various fields, particularly in healthcare, where data privacy concerns limit the availability of real datasets for research and analysis. This paper presents an enhanced Conditional Generative Adversarial Network (GAN) architecture designed for generating high-quality synthetic tabular data, with a focus on cardiovascular disease datasets that encompass mixed data types and complex feature relationships. The proposed architecture employs specialized sub-networks to process continuous and categorical variables separately, leveraging metadata such as Gaussian Mixture Model (GMM) parameters for continuous attributes and embedding layers for categorical features. By integrating these specialized pathways, the generator produces synthetic samples that closely mimic the statistical properties of the real data. Comprehensive experiments were conducted to compare the proposed architecture with two established models: Conditional Tabular GAN (CTGAN) and Tabular Variational AutoEncoder (TVAE). The evaluation utilized metrics such as the Kolmogorov-Smirnov (KS) test for continuous variables, the Jaccard coefficient for categorical variables, and pairwise correlation analyses. Results indicate that the proposed approach attains a mean KS statistic of 0.3900, demonstrating strong overall performance that outperforms CTGAN (0.4803) and is comparable to TVAE (0.3858). Notably, our approach shows lowest KS statistics for key continuous features, such as total cholesterol (KS = 0.0779), weight (KS = 0.0861), and diastolic blood pressure (KS = 0.0957), indicating its effectiveness in closely replicating real data distributions. Additionally, it achieved a Jaccard coefficient of 1.00 for eight out of eleven categorical variables, effectively preserving categorical distributions. These findings indicate that the proposed architecture captures both distributions and dependencies, providing a robust solution in supporting mobile personalized cardiovascular disease prevention systems.

Segmentation of Plain CT Image of Ischemic Lesion based on Trans-Swin-UNet

The present study aims to build a hybrid convolutional neural network and transformer UNet-based model, Trans-Swin-UNet, to segment ischemic lesions of the plain computed tomography (CT) image. The model architecture is built based on TransUnet and has four main improvements. First, replace the decoder of TransUNet with a Swin transformer; second, add a Max Attention module into the skip connection; third, design a comprehensive loss function; and last, speed up the segmentation performance. The present study designs two experiments to evaluate the performance of the built model using both the self-collected and public plain CT image datasets. The model optimization experiment evaluates the improvements of Trans-Swin-UNet over TransUnet. The experimental results show that each improvement of the built model can achieve a better performance than TransUNet in terms of dice similarity coefficient (DSC), Jaccard coefficient (JAC), and accuracy (ACC). The comparison experiment compares the built model with four existing UNet-based models. The experimental results show that the built model had a DSC of 0.72±0.01, a JAC of 0.78±0.04, an ACC of 0.75±0.03 using the self-collected plain CT image dataset and a DSC of 0.73±0.02, a JAC of 0.79±0.03, an ACC of 0.76±0.02 using the public plain CT image dataset, achieving the best segmentation performance among five UNet-based neural network models. The two experimental results conclude that the built model could accurately segment ischemic lesions of the plain CT image. The limitations and future work of this study are also discussed.

MediLite3DNet: A lightweight network for segmentation of nasopharyngeal airways.

The precise segmentation and three-dimensional reconstruction of the nasopharyngeal airway are crucial for the diagnosis and treatment of adenoid hypertrophy in children. However, traditional methods face challenges such as information loss and low computational efficiency when addressing this task. To overcome these issues, this paper introduces an innovative lightweight 3D medical image segmentation network-MediLite3DNet. The core of this network is the Parallel Multi-Scale High-Resolution Network (PMHNet), which effectively retains detailed features of the airway and optimizes the fusion of multi-scale features through its parallel structure. In response to the complexity of existing networks and their reliance on vast amounts of training data, this paper presents an efficient Hierarchical Decoupled Convolution Module (EHDC) to reduce computational costs while maintaining efficient feature extraction capabilities. Furthermore, to enhance the accuracy of segmentation, a lightweight Channel and Spatial Attention Mechanism (LCSA) is proposed. This mechanism identifies and emphasizes key channels and spatial features, improving the processing of complex medical images while controlling the increase in the number of parameters. Experiments conducted on a clinical CT dataset demonstrate the network's exceptional performance, with a Dice coefficient of 97.42%, sensitivity of 98.69%, and Jaccard index of 95%. Maintaining high precision, the model has a parameter count of only 0.227M and a floating-point operation count (FLOPs) of 24.526G, proving its computational efficiency. The significance of this study is that it provides a highly efficient and accurate diagnostic tool for children with adenoid hypertrophy. Additionally, with the innovative MediLite3DNet design, it brings a new lightweight solution to the domain of medical image segmentation.

CFF-Net: Cross-Hierarchy Feature Fusion Network Based on Composite Dual-Channel Encoder for Surface Defect Segmentation

In industries spanning manufacturing to software development, defect segmentation is essential for maintaining high standards of product quality and reliability. However, traditional segmentation methods often struggle to accurately identify defects due to challenges like noise interference, occlusion, and feature overlap. To solve these problems, we propose a cross-hierarchy feature fusion network based on a composite dual-channel encoder for surface defect segmentation, called CFF-Net. Specifically, in the encoder of CFF-Net, we design a composite dual-channel module (CDCM), which combines standard convolution with dilated convolution and adopts a dual-path parallel structure to enhance the model’s capability in feature extraction. Then, a dilated residual pyramid module (DRPM) is integrated at the junction of the encoder and decoder, which utilizes the expansion convolution of different expansion rates to effectively capture multi-scale context information. In the final output phase, we introduce a cross-hierarchy feature fusion strategy (CFFS) that combines outputs from different layers or stages, thereby improving the robustness and generalization of the network. Finally, we conducted comparative experiments to evaluate CFF-Net against several mainstream segmentation networks across three distinct datasets: a publicly available Crack500 dataset, a self-built Bearing dataset, and another publicly available SD-saliency-900 dataset. The results demonstrated that CFF-Net consistently outperformed competing methods in segmentation tasks. Specifically, in the Crack500 dataset, CFF-Net achieved notable performance metrics, including an Mcc of 73.36%, Dice coefficient of 74.34%, and Jaccard index of 59.53%. For the Bearing dataset, it recorded an Mcc of 76.97%, Dice coefficient of 77.04%, and Jaccard index of 63.28%. Similarly, in the SD-saliency-900 dataset, CFF-Net achieved an Mcc of 84.08%, Dice coefficient of 85.82%, and Jaccard index of 75.67%. These results underscore CFF-Net’s effectiveness and reliability in handling diverse segmentation challenges across different datasets.

Integrated MADM approach based on extended MABAC method with Aczel–Alsina generalized weighted Bonferroni mean operator

Currently, q-rung orthopair (q-ROF) set theory is one of the most effective set theories in dealing uncertainty associated with imprecise information. In complex decision-making problems, input variables can be described by q-ROF numbers to cope ambiguity. While, generalized weighted Bonferroni mean (GWBM) operator can reflect correlation among input arguments. Aczel–Alsina operations underline fair and accurate evaluation of decision-makers. Harnessing these benefits, a pioneering extension of the GWBM operator based on Aczel–Alsina operations is introduced. Simultaneously, a novel generalized distance measure is crafted, drawing inspiration from Dice and Jaccard similarities. Beside these, using stepwise weight assessment ratio analysis (SWARA) and multi-attribute border approximation area comparison (MABAC) methods, this study pioneers an integrated method, q-ROF-SWARA-MABAC for assessing and prioritizing factors and alternatives on q-ROF environment. Later, with the suggested model, a case study on high-speed rail corridor (HSRC) for India is solved, revealing Varanasi-Howrah HSRC as the most preferable choice.. Moving forward, detailed sensitive analysis of suggested model is performed to explore the pertinence and supremacy. Eventually, the outcomes manifest that novel framework is flexible, reliable, accurate and could be viable option to consider for future use.

Clustering of cognitive domains among older persons with HIV.

Age and HIV are synergistic risk factors for conditions such as HIV-associated neurocognitive disorders (HAND). Yet, it is unclear whether older persons with HIV (OPWH) display different cognitive profiles for HAND. To describe the cognitive patterns of OPWH treated with combined antiretroviral therapy (cART). Cross-sectional study that included 330 participants with HIV, aged 50 years or older, cared for at a tertiary care hospital in Mexico City. A short neuropsychological test battery was used to assess a wide spectrum of cognitive functions. The optimal number of cognitive clusters was determined by the silhouette method and a minimization of the Bayesian information criterion. Participants' mean age was 58.8 years (standard deviation = 6.6), and 12.1% were women. A 3-cluster solution yielded stable Jaccard coefficients (p > 0.70). Cluster 1 showed more significant impairment in visual and verbal memory domains, whereas participants in cluster 3 showed significant impairment in language, and abstraction. Cluster 2 showed no predominance of any domain for alterations. There are different cognitive profiles among OAWH with HAND. These differences may be due to individual patterns of HIV-related and non-HIV-related factors.

An automatic approach to detect skin cancer utilizing active infrared thermography

Skin cancer is a growing global concern, with cases steadily rising. Typically, malignant moles are identified through visual inspection, using dermatoscopy and patient history. Active thermography has emerged as an effective method to distinguish between malignant and benign lesions. Our previous research showed that spatio-temporal features can be extracted from suspicious lesions to accurately determine malignancy, which was applied in a distance-based classifier.In this study, we build on that foundation by introducing a set of novel spatial and temporal features that enhance classification accuracy and can be integrated into any machine learning approach. These features were implemented in a support-vector machine classifier to detect malignancy. Notably, our method addresses a common limitation in existing approaches—manual lesion selection—by automating the process using a U-Net convolutional neural network.We validated our system by comparing U-Net's performance with expert dermatologist segmentations, achieving a 17% improvement in the Jaccard index over a semi-automatic algorithm. The detection algorithm relies on accurate lesion segmentation, and its performance was evaluated across four segmentation techniques. At an 85% sensitivity threshold, expert segmentation provided the highest specificity at 87.62%, while non-expert and U-Net segmentations achieved comparable results of 69.63% and 68.80%, respectively. Semi-automatic segmentation lagged behind at 64.45%. This automated detection system performs comparably to high-accuracy methods while offering a more standardized and efficient solution. The proposed automatic system achieves 3% higher accuracy compared to the ResNet152V2 network when processing low-quality images obtained in a clinical setting.

Deep learning-enhanced automated mitochondrial segmentation in FIB-SEM images using an entropy-weighted ensemble approach.

Mitochondria are intracellular organelles that act as powerhouses by breaking down nutrition molecules to produce adenosine triphosphate (ATP) as cellular fuel. They have their own genetic material called mitochondrial DNA. Alterations in mitochondrial DNA can result in primary mitochondrial diseases, including neurodegenerative disorders. Early detection of these abnormalities is crucial in slowing disease progression. With recent advances in data acquisition techniques such as focused ion beam scanning electron microscopy, it has become feasible to capture large intracellular organelle volumes at data rates reaching 4Tb/minute, each containing numerous cells. However, manually segmenting large data volumes (gigapixels) can be time-consuming for pathologists. Therefore, there is an urgent need for automated tools that can efficiently segment mitochondria with minimal user intervention. Our article proposes an ensemble of two automatic segmentation pipelines to predict regions of interest specific to mitochondria. This architecture combines the predicted outputs from both pipelines using an ensemble learning-based entropy-weighted fusion technique. The methodology minimizes the impact of individual predictions and enhances the overall segmentation results. The performance of the segmentation task is evaluated using various metrics, ensuring the reliability of our results. We used four publicly available datasets to evaluate our proposed method's effectiveness. Our proposed fusion method has achieved a high score in terms of the mean Jaccard index and dice coefficient for all four datasets. For instance, in the UroCell dataset, our proposed fusion method achieved scores of 0.9644 for the mean Jaccard index and 0.9749 for the Dice coefficient. The mean error rate and pixel accuracy were 0.0062 and 0.9938, respectively. Later, we compared it with state-of-the-art methods like 2D and 3D CNN algorithms. Our ensemble approach shows promising segmentation efficiency with minimal intervention and can potentially aid in the early detection and mitigation of mitochondrial diseases.

DDANet: A deep dilated attention network for intracerebral haemorrhage segmentation.

Intracranial haemorrhage (ICH) is an urgent and potentially fatal medical condition caused by brain blood vessel rupture, leading to blood accumulation in the brain tissue. Due to the pressure and damage it causes to brain tissue, ICH results in severe neurological impairment or even death. Recently, deep neural networks have been widely applied to enhance the speed and precision of ICH detection yet they are still challenged by small or subtle hemorrhages. The authors introduce DDANet, a novel haematoma segmentation model for brain CT images. Specifically, a dilated convolution pooling block is introduced in the intermediate layers of the encoder to enhance feature extraction capabilities of middle layers. Additionally, the authors incorporate a self-attention mechanism to capture global semantic information of high-level features to guide the extraction and processing of low-level features, thereby enhancing the model's understanding of the overall structure while maintaining details. DDANetalso integrates residual networks, channel attention, and spatial attention mechanisms for joint optimisation, effectively mitigating the severe class imbalance problem and aiding the training process. Experiments show that DDANet outperforms current methods, achieving the Dice coefficient, Jaccard index, sensitivity, accuracy, and a specificity of 0.712, 0.601, 0.73, 0.997, and 0.998, respectively. The code is available at https://github.com/hpguo1982/DDANet.

Enhancing multilabel classification for unbalanced COVID-19 vaccination hesitancy tweets using ensemble learning

Purpose:Vaccination plays a crucial role in public health strategies, particularly in combating contagious diseases like COVID-19. Despite developing and authorizing numerous vaccines, achieving widespread immunity remains a challenge. Understanding individuals’ willingness to receive vaccines is essential, and recent studies have explored acceptance rates across various regions. However, vaccine hesitancy is still considered a significant global health concern, even after the pandemic’s peak. Methods:This article explores the topic of classification in social media networks, specifically in tweets, focusing on the challenges of class imbalance and multilabel multiclass tweet classification. It investigates the effectiveness of oversampling in addressing unbalanced datasets and proposes an ensemble-based framework comprising various BERT models fine-tuned on diverse text corpora. Results:The proposed model was applied to a benchmark dataset containing 12 classes with a highly imbalanced distribution. Compared to recent literature, the model achieved significant improvements in performance metrics. We observed a 2% increase in micro, macro, and weighted average F1 scores, a 4% increase in accuracy, and a 3% increase in average Jaccard index when using the proposed framework on this unbalanced multi-labeled benchmark. Conclusion:The study investigated fine-tuning BERT models for improved classification in tweets on unbalanced datasets related to public health. The proposed ensemble approach with oversampling techniques yielded significant improvements in multiple metrics compared to existing methods. This suggests a promising approach to analyzing vaccine discourse on social media and potentially aiding public health efforts.