Image-based Analysis Research Articles

A quantitative, image-based analysis of the spreading performance of PolyArylEtherKetone polymer powders during Powder Bed Fusion Additive Manufacture

Powder spreadability in the powder bed fusion process is normally visually assessed by the machine operator through repeat trials at room temperature or elevated temperatures. Some studies used powder rheology results as an indicator of the powder spreadability. This study presents a novel method of image-based analysis for the assessment of polymer spreading quality for powder bed fusion and demonstrates that the subjective assessment of the machine operator can be replaced by quantitative and measurable data. Twenty-four developmental grade PolyArylEtherKetone powders were tested. Images of the powder bed surface were analysed in MATLAB and the relationships with the powder rheology established through statistical analysis. The two methods for calculating surface deviations from variations in the greyscale images showed to be sensitive to the recoater travel and presented a strong correlation with the Normalised Aeration Sensitivity (NAS), a powder rheology parameter identified in a previous study as the most significant parameter able to categorise powder flow and spreadability based on a yes/no response.

IAMSAM: image-based analysis of molecular signatures using the Segment Anything Model

Spatial transcriptomics is a cutting-edge technique that combines gene expression with spatial information, allowing researchers to study molecular patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment Anything Model, allowing for the semi-automatic selection of regions of interest based on morphological signatures. Furthermore, IAMSAM provides downstream analysis, such as identifying differentially expressed genes, enrichment analysis, and cell type prediction within the selected regions. With its simple interface, IAMSAM empowers researchers to explore and interpret heterogeneous tissues in a streamlined manner.

Segmentation uncertainty of vegetated porous media propagates during X-ray CT image-based analysis

Abstract Background and Aims Phase segmentation is a crucial step in X-ray computed tomography (CT) for image-based analysis (CT-IBA) to derive soil and root information. How segmentation uncertainty (SU) affects CT-IBA of vegetated soil has never been explored. Methods We proposed a new framework enabled by machine learning to measure SU and its propagation from the first to the second-order parameters derived from CT-IBA. Vegetated glass beads of varying moisture contents and plant species were CT scanned. Segmented images were used to determine volumetric fractions and morphological properties of each phase for determining the absolute permeability (K). Results Although the root phase is susceptible to SU, its influence on CT-IBA is minimal when the root content is low. However, its SU was magnified when the water phase is present. The grain phase has a lower SU susceptibility, but due to its large volumetric content, the IBA can be affected significantly. Fine roots were found to exhibit higher SU than coarse roots, indicating that root architecture has an effect on the segmentation of the root phase, and thus higher-order properties like K. Conclusion Segmentation of the grain phase is sensitive to SU. A small SU will lead to a remarkably erroneous estimation of pore morphological properties and K. To reduce SU, we suggest reducing the water content to a discontinuous state of a cohesionless vegetated porous media specimen before sending it for CT scanning and IBA. However, caution should be taken when fine roots were dried and experienced excessive shrinkage.

Theoretical study of surface-enhanced Raman spectroscopy of dimethyl methylphosphonate

In this study, the surface-enhanced Raman spectroscopy spectra of dimethyl methyl phosphonate, which serves as a surrogate for the chemical warfare agent sarin, were modeled using density functional theory calculations. In addition, the fundamental mechanisms underlying the enhancement of surface-enhanced Raman spectroscopy are investigated. A comprehensive examination was conducted of the adsorption behavior and Surface-Enhanced Raman Spectroscopy enhancement effects resulting from the interaction between dimethyl methylphosphonate and silver clusters. Moreover, a comparison was made between the findings of the present study and the results of previous experiments in order to ascertain the optimal positions for the adsorption of silver clusters on dimethyl methylphosphonate molecules. In this article, the kinetics of the interaction between dimethyl methylphosphonate and silver clusters were theoretically explored through six different adsorption configurations. It was found that the configuration involving the dimethyl methylphosphonate structure and silver clusters containing three silver atoms exhibited the highest stability. By integrating Raman spectroscopic data with our theoretical insights, the vibrational modes, Raman spectra, and surface-enhanced Raman spectra of dimethyl methylphosphonate molecules were systematically correlated using Gaussian 09 and GaussView 5.0.8 software for detailed image-based analysis.

Estimation of Physico-Chemical Properties of Soil Using Machine Learning

This study leverages soil images to determine its physico-chemical characteristics from two locations in Benue State, North-Central Nigeria: Vandeikya (7.099653°, 8.572102°) and Konshisha (6.783539°, 9.069549°). Practical alternatives to traditional soil quality estimation methods, often costly and inaccessible in resource-poor regions, were implemented. The study integrates Machine Learning (ML) and Deep Learning (DL) techniques with soil image analysis, employing image feature extraction through Gray-Level Co-occurrence Matrix (GLCM) and Gabor filters to enhance the feature space across 1,388 samples (each consisting of 600g of soil) and 16 properties. We evaluated different models, including Support Vector Regression (SVR), a Convolutional Neural Network (CNN), an optimized CNN using Grid Search, and a stacked ML-CNN approach. The models were assessed on their ability to predict soil properties while minimizing error rates. The SVR model showed limited predictive performance, with its best results yielding slightly over 40% R² and negative scores for physical properties. The CNN model performed better but had feature scores below 80% R². The optimized CNN model surpassed 90% R² and reduced MAE by more than 50% for several properties. The stacked ML-CNN model further improved these metrics, demonstrating the effectiveness of the integrated approach. This research highlights the potential for using machine learning and image-based analysis as a scalable, cost-effective alternative to conventional soil testing, offering practical solutions for regions where conventional measurement tools are scarce. Integration with historical soil data might also open new possibilities for enhancing the accuracy and applicability of these methods for broader soil management practices.

Image-Based Quantitative Analysis of Epidermal Morphology in Wild Potato Leaves.

The epidermal leaf patterns of plants exhibit remarkable diversity in cell shapes, sizes, and arrangements, driven by environmental interactions that lead to significant adaptive changes even among closely related species. The Solanaceae family, known for its high diversity of adaptive epidermal structures, has traditionally been studied using qualitative phenotypic descriptions. To advance this, we developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology. Applied to nine wild potato species, this workflow quantified key morphological parameters, identifying descriptors for trichomes, stomata, and pavement cells, and revealing interdependencies among these traits. Principal component analysis (PCA) highlighted two main axes, accounting for 45% and 21% of variance, corresponding to features such as guard cell shape, trichome length, stomatal density, and trichome density. These axes aligned well with the historical and geographical origins of the species, separating southern from Central American species, and forming distinct clusters for monophyletic groups. This workflow thus establishes a quantitative foundation for investigating leaf epidermal cell morphology within phylogenetic and geographic contexts.

Simple methodology to score micropattern quality and effectiveness.

Micropatterns (MPs) are widely used as a powerful tool to control cell morphology and phenotype. However, methods for determining the effectiveness of how well cells are controlled by the shape of MPs have been inconsistently used and studies rarely report on this topic, indicating lack of standardization. We introduce an evaluation score that quantitatively assesses the MP fabrication quality and effectiveness, which can be broadly used in conjunction with all currently available MP design types. This score uses four simple and quick steps: (i) scoring MP and (ii) background fabrication quality, (iii) defining the type(s) of MP of interest, and (iv) assigning so-called efficiency descriptors describing cell behavior. These steps are based on visual inspection and quick categorization of various aspects of MP fabrication quality and cell behaviour, presented in illustrations and microscopy image examples intended to serve as a reference "atlas". To illustrate the advantage of using this score, we determined differences in cell morphology and F-actin intensity between scored vs. non-scored cells. These measurements, which could be different in other studies, were chosen because both are understood as markers of cell phenotype and function. We combined intensity-calibrated immunofluorescence microscopy and image-based single cell protein analysis. Importantly, significant differences in cell morphology and cytoskeletal protein content between scored vs. non-scored cells were noted: the unconditional inclusion of all experimental read-outs (i.e., all MP data regardless of MP quality and effectiveness) into the final results significantly misjudged the experimental readouts vs. only including experimental read-outs of quality-controlled and effective MPs, identified by scoring. Specifically, non-scoring underestimated the F-actin intensity per cell and quantitative cellular morphometric descriptors circularity and solidity and overestimated aspect ratio. Scoring improved the precision of cellular readouts, advocating the use of a MP quality and efficiency score as a quantitative decision-supporting tool in deciding whether or not particular MPs should be used for experiments, saving time and money. This simple scoring methodology can be used for improving MP fabrication, comparing results across studies, benefiting basic science studies and potential future clinical use of MPs by introducing standardization.

Image-Based Analysis Method for Detecting a Rebar in Concrete Using Short-Time Fourier Transform (STFT)

Image-Based Analysis Method for Detecting a Rebar in Concrete Using Short-Time Fourier Transform (STFT)

Diagnostic performance of deep learning for infectious keratitis: a systematic review and meta-analysis

Infectious keratitis (IK) is the leading cause of corneal blindness globally. Deep learning (DL) is an emerging tool for medical diagnosis, though its value in IK is unclear. We aimed to assess the diagnostic accuracy of DL for IK and its comparative accuracy with ophthalmologists. In this systematic review and meta-analysis, we searched EMBASE, MEDLINE, and clinical registries for studies related to DL for IK published between 1974 and July 16, 2024. We performed meta-analyses using bivariate models to estimate summary sensitivities and specificities. This systematic review was registered with PROSPERO (CRD42022348596). Of 963 studies identified, 35 studies (136,401 corneal images from >56,011 patients) were included. Most studies had low risk of bias (68.6%) and low applicability concern (91.4%) in all domains of QUADAS-2, except the index test domain. Against the reference standard of expert consensus and/or microbiological results (seven external validation studies; 10,675 images), the summary estimates (95% CI) for sensitivity and specificity of DL for IK were 86.2% (71.6-93.9) and 96.3% (91.5-98.5). From 28 internal validation studies (16,059 images), summary estimates for sensitivity and specificity were 91.6% (86.8-94.8) and 90.7% (84.8-94.5). Based on seven studies (4007 images), DL and ophthalmologists had comparable summary sensitivity [89.2% (82.2-93.6) versus 82.2% (71.5-89.5); P=0.20] and specificity [(93.2% (85.5-97.0) versus 89.6% (78.8-95.2); P=0.45]. DL models may have good diagnostic accuracy for IK and comparable performance to ophthalmologists. These findings should be interpreted with caution due to the image-based analysis that did not account for potential correlation within individuals, relatively homogeneous population studies, lack of pre-specification of DL thresholds, and limited external validation. Future studies should improve their reporting, data diversity, external validation, transparency, and explainability to increase the reliability and generalisability of DL models for clinical deployment. NIH, Wellcome Trust, MRC, Fight for Sight, BHP, and ESCRS.

Label-free long-term measurements of adipocyte differentiation from patient-driven fibroblasts and quantitative analyses of in situ lipid droplet generation

The visualization and tracking of adipocytes and their lipid droplets (LDs) during differentiation are pivotal in developmental biology and regenerative medicine studies. Traditional staining or labeling methods, however, pose significant challenges due to their labor-intensive sample preparation, potential disruption of intrinsic cellular physiology, and limited observation timeframe. This study introduces a novel method for long-term visualization and quantification of biophysical parameters of LDs in unlabeled adipocytes, utilizing the refractive index (RI) distributions of LDs and cells. We employ low-coherence holotomography (HT) to systematically investigate and quantitatively analyze the 42-day redifferentiation process of fat cells into adipocytes. This technique yields three-dimensional, high-resolution refractive tomograms of adipocytes, enabling precise segmentation of LDs based on their elevated RI values. Subsequent automated analysis quantifies the mean concentration, volume, projected area, and dry mass of individual LDs, revealing a gradual increase corresponding with adipocyte maturation. Our findings demonstrate that HT is a potent tool for non-invasively monitoring live adipocyte differentiation and analyzing LD accumulation. This study, therefore, offers valuable insights into adipogenesis and lipid research, establishing HT and image-based analysis as a promising approach in these fields.

Toward assessment of rupture risk predictors in abdominal aortic aneurysms including intraluminal thrombus based on 3D+t ultrasound images.

Image-based patient-specific rupture risk analysis for abdominal aortic aneurysms (AAAs) has shown considerable promise. However, clinical translation has been hampered by the use of invasive and costly imaging modalities. Despite being a promising alternative, ultrasound (US) makes a full analysis, including intraluminal thrombus (ILT), not trivial. This study explored the feasibility of assessing AAA rupture risk parameters, e.g., peak wall stress (PWS) and peak wall rupture index (PWRI), using US-based models of the AAA wall, finally including ILT. Three-dimensional US data were segmented from a group of AAA patients whose CT data were available within 30 days. The segmented vessel wall and ILT boundaries were converted into a mesh including and excluding ILT to evaluate the effect of adding ILT on the model output. US-based rupture risk parameters (PWS and PWRI) were compared to CT-based results. The US-based PWS and PWRI, including ILT, showed good agreement with CT-based results, and the model excluding ILT showed no significant bias in wall stress or rupture index. When including ILT, a lower US-based wall stress and rupture index of 7.2% and 3.8% were found, respectively. The intraclass correlation coefficient (ICC) of PWS was 0.60. The highest ICC was found for the PWRI (ICC = 0.86), indicating good absolute agreement. This study showed that PWRI can be estimated with US when including the ILT, yielding comparable results to CT, and good absolute agreement. Future work should focus on improving the contrast of ILT in US, since this will be essential to performing large-scale studies in AAA cohorts.

An image-based investigation on color associations among 100 Chinese emotion words

ABSTRACT Image data serves as a valuable resource for investigating relationships between colors and emotions. This study conducts an image-based visual corpus analysis on the color associations of 100 Chinese emotion words, aiming to uncover the pivotal roles of colors in understanding emotional concepts. The study addresses two primary objectives: (i) examining the interrelations among four affective properties (valence, arousal, prototypicality, and emotionality) and four image-based color attributes (Jz: a dimension depicting black–white color distinction, Az: a dimension for green-red, Bz: a dimension for blue–yellow, and color variability) associated with these emotion words; and (ii) assessing the efficacy of image-based color information in profiling affective (dis)similarities among different emotion words. The empirical results reveal (i) significant positive correlations between color variability and arousal, Jz and valence, Az and arousal, Bz and valence, as well as a negative correlation between Jz and prototypicality; (ii) the effectiveness of image-based color information in depicting the valence-dominated affective (dis)similarities among the 100 Chinese emotion words. This study contributes image-based empirical support to complement existing research on color-emotion mappings. Moreover, it advances methodological approaches by advocating for the utilisation of image data to address theoretical inquiries in cognitive science.

A Hybrid Model for Palm Sugar Type Classification: Advancing Image-Based Analysis for Industry Applications

A Hybrid Model for Palm Sugar Type Classification: Advancing Image-Based Analysis for Industry Applications

CAM: a novel aid system to analyse the coloration quality of thick blood smears using image processing and machine learning techniques.

Battling malaria's morbidity and mortality rates demands innovative methods related to malaria diagnosis. Thick blood smears (TBS) are the gold standard for diagnosing malaria, but their coloration quality is dependent on supplies and adherence to standard protocols. Machine learning has been proposed to automate diagnosis, but the impact of smear coloration on parasite detection has not yet been fully explored. To develop Coloration Analysis in Malaria (CAM), an image database containing 600 images was created. The database was randomly divided into training (70%), validation (15%), and test (15%) sets. Nineteen feature vectors were studied based on variances, correlation coefficients, and histograms (specific variables from histograms, full histograms, and principal components from the histograms). The Machine Learning Matlab Toolbox was used to select the best candidate feature vectors and machine learning classifiers. The candidate classifiers were then tuned for validation and tested to ultimately select the best one. This work introduces CAM, a machine learning system designed for automatic TBS image quality analysis. The results demonstrated that the cubic SVM classifier outperformed others in classifying coloration quality in TBS, achieving a true negative rate of 95% and a true positive rate of 97%. An image-based approach was developed to automatically evaluate the coloration quality of TBS. This finding highlights the potential of image-based analysis to assess TBS coloration quality. CAM is intended to function as a supportive tool for analyzing the coloration quality of thick blood smears.

Ultrashort Oncologic Whole-Body [18F]FDG Patlak Imaging Using LAFOV PET.

Methods to shorten [18F]FDG Patlak PET imaging procedures ranging from 65-90 to 20-30 min after injection, using a population-averaged input function (PIF) scaled to patient-specific image-derived input function (IDIF) values, were recently evaluated. The aim of the present study was to explore the feasibility of ultrashort 10-min [18F]FDG Patlak imaging at 55-65 min after injection using a PIF combined with direct Patlak reconstructions to provide reliable quantitative accuracy of lung tumor uptake, compared with a full-duration 65-min acquisition using an IDIF. Methods: Patients underwent a 65-min dynamic PET acquisition on a long-axial-field-of-view (LAFOV) Biograph Vision Quadra PET/CT scanner. Subsequently, direct Patlak reconstructions and image-based (with reconstructed dynamic images) Patlak analyses were performed using both the IDIF (time to relative kinetic equilibrium between blood and tissue concentration (t*) = 30 min) and a scaled PIF at 30-60 min after injection. Next, direct Patlak reconstructions were performed on the system console using only the last 10 min of the acquisition, that is, from 55 to 65 min after injection, and a scaled PIF using maximum crystal ring difference settings of both 85 and 322. Tumor lesion and healthy-tissue uptake was quantified and compared between the differently obtained parametric images to assess quantitative accuracy. Results: Good agreement was obtained between direct- and image-based Patlak analyses using the IDIF (t* = 30 min) and scaled PIF at 30-60 min after injection, performed using the different approaches, with no more than 8.8% deviation in tumor influx rate value (Ki ) (mean difference ranging from -0.0022 to 0.0018 mL/[min × g]). When direct Patlak reconstruction was performed on the system console, excellent agreement was found between the use of a scaled PIF at 30-60 min after injection versus 55-65 min after injection, with 2.4% deviation in tumor Ki (median difference, -0.0018 mL/[min × g]; range, -0.0047 to 0.0036 mL/[min × g]). For different maximum crystal ring difference settings using the scan time interval of 55-65 min after injection, only a 0.5% difference (median difference, 0.0000 mL/[min × g]; range, -0.0004 to 0.0013 mL/[min × g]) in tumor Ki was found. Conclusion: Ultrashort whole-body [18F]FDG Patlak imaging is feasible on an LAFOV Biograph Vision Quadra PET/CT system without loss of quantitative accuracy to assess lung tumor uptake compared with a full-duration 65-min acquisition. The ultrashort 10-min direct Patlak reconstruction with PIF allows for its implementation in clinical practice.

Hybrid modes in multilayer/antenna architecture set side-band-selective molecular Raman scattering

Abstract Technologies with tag-less capacity for analyzing molecules, are poised to make significant advancements in the healthcare industry with a substantial potential to improve sensitivity for individual molecules and binding events. Plasmonics has the potential for enhanced system response when emitters are included in the full architecture. Here, a hybrid multilayer including a sequence of metals and dielectrics has been examined. The surface of the multilayer is covered with 30-μm height features working as plasmonic antennae. Their contribution to the emission of the system has been analyzed. The multilayer has been coupled with a prism to excite the polaritons in an experimental optical setup to measure the reflected and transmitted signals. The measurements demonstrate the surface plasmon polariton/antenna mode hybridization. The optomechanics of the plasmonic resonant multilayer has been studied with reference to the refractive index of the surrounding medium as well as to the incident angle of the exciting beam. Then, the experimental shifts have been measured according to the optomechanical spectrum of the naïve plasmon resonant multilayer. The optomechanics of the plasmonic resonant multilayer, which has shown a natural resonance at ω = 96 MHz , has been coupled with a molecular emitter, e.g. a dye. As the concentration of the red dye increases, the intensity of the peaks at ω > 96 MHz raises, suggesting an important sensitivity in the anti-Stokes domain. On the other side, the decay is equal to κ = 1.1 × 10 − 2 MHz showcasing a high quality factor ( Q = 9.6 × 10 3 ). For validation of increased sensitivity in the anti-Stokes region, bovine serum albumin (BSA), a protein active in the Raman region, has been tested and, through an image-based analysis, the molecular pattern recorded. The demonstrated potential of utilizing optical resonance shifts to investigate molecular patterns is highly promising. Therefore, the proposed sensing method represents a significant advancement in the field, offering new opportunities for the sensitive detection of biomolecules.

Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation

Convolutional neural networks (CNNs) achieve prevailing results in segmentation tasks nowadays and represent the state-of-the-art for image-based analysis. However, the under- standing of the accurate decision-making process of a CNN is rather unknown. The research area of explainable artificial intelligence (xAI) primarily revolves around understanding and interpreting this black-box behavior. One way of interpreting a CNN is the use of class ac- tivation maps (CAMs) that represent heatmaps to indicate the importance of image areas for the prediction of the CNN. For classification tasks, a variety of CAM algorithms exist. But for segmentation tasks, only one CAM algorithm for the interpretation of the output of a CNN exist. We propose a transfer between existing classification- and segmentation- based methods for more detailed, explainable, and consistent results which show salient pixels in semantic segmentation tasks. The resulting Seg-HiRes-Grad CAM is an exten- sion of the segmentation-based Seg-Grad CAM with the transfer to the classification-based HiRes CAM. Our method improves the previously-mentioned existing segmentation-based method by adjusting it to recently published classification-based methods. Especially for medical image segmentation, this transfer solves existing explainability disadvantages. The code is available at <a href='https://github.com/TillmannRheude/SegHiResGrad_CAM'>https://github.com/TillmannRheude/SegHiResGrad_CAM</a>

Longitudinal Assessment of Retinopathy of Prematurity (LONGROP) Study: Impacts of Viewing Time and Ability to Compare on Detection of Change

This study compared two imaging grading techiques to assess the utility of longitudinal image-based analysis in retinopathy of prematurity (ROP) screening: 1) time-limited without image comparison (a proxy for bedside indirect ophthalmoscopy, termed sBIO) and time-unlimited with image comparison (for telemedicine grading, termed TELE) screening. We tested two hypotheses: 1) H1: TELE was superior to sBIO for the detection of change (Tempo)-same, better, or worse-and, 2) H2: granular data of change (e.g. at the image and feature level) is integrated by graders to achieve the Tempo assessment. Prospective reliability analysis. Gold standard reference (GS) was a published curated ROP image database consisting of both Tempo and granular level changes (image and components) from 40 patients in 2 sets. Graders were divided into 2 cohorts. There were two screening techniques-1) sBIO with time limited review of 10 minutes/patient, access to prior notes and drawings and 2) TELE with unlimited review time, access to prior weeks' images, notes and schematics. Graders switched techniques and sets after 6 weeks. H1 outcome was comparison of graders' weekly Tempo scores to GS-Gestalt and for H2 was Tempo score compared to GS-View and GS-Component. H1 demonstrated no difference-accuracy of sBIO and TELE compared to GS was 51.7% and 51.9% respectively (p=0.95). Highest agreement occurred when all exams exhibited no change (91.5% sBIO vs. 93.5% TELE, p=0.46) and worst agreement was when exams always demonstrated worsening (46.5% sBIO vs. 47.1% TELE, p=0.93). Both sets of graders did worse in weeks 7-12, irrespective of technique. H2 demonstrated that Tempo assessment did not correlate with granular data changes in the GS for View level and Component level assessments-overall agreement dropped to 31.4% for Tempo vs GS-VIEW (31.2% for sBIO, 31.5% for TELE) and 4.6% for Tempo vs GS-COMPONENT (4.9% for sBIO, 4.3% for TELE). Detection of ROP Tempo was independent of screening technique by expert pediatric retina graders. Both groups did significantly better in the first half of the study, indicative of a fatigue factor. This is the first study in ROP history to demonstrate that graders integrate image and retinal features in various ways that can be in contradiction of their assessment of overall disease progression.

Image-based PM10 Concentration Classification with Convolutional Neural Network

Air pollution is a crucial environmental problem. One of the air quality indicators is the concentration of PM10, which is particulate matter less than 10 microns in size. Unfortunately, PM10 monitoring in Indonesia is only available at 15 observation stations. In fact, the availability of PM10 information in real time is important for pollution control and health protection. Therefore, this research focuses on sky image-based PM10 concentration analysis by utilizing photos taken using smartphone cameras. By applying CNN, PM10 concentration estimation can be done in real time. To achieve the goal, a total of 300 image data were retrieved from Beijing tourism web along with PM10 attributes. The images were classified into three categories: 'good', 'moderate', and 'unhealthy'. A total of 80% of the data is used to train six variations of CNN models. Furthermore, the model with the highest accuracy will be selected as the best model. The results show that CNN architecture with Leaky ReLU activation function and average pooling is valid to classify images based on PM10 concentration. The results of this study can be a powerful tools for improving public health and reducing the impact of air pollution.

Neurovisual Mapping: Image-Based EEG Analysis and Classification of Motor Imagery Patterns for BCI Applications

Neurovisual Mapping: Image-Based EEG Analysis and Classification of Motor Imagery Patterns for BCI Applications