User-friendly Graphical Interface Research Articles

GWmodelS: a standalone software to train geographically weighted models

ABSTRACT With the recent increase in studies on spatial heterogeneity, geographically weighted (GW) models have become an essential set of local techniques, attracting a wide range of users from different domains. In this study, we demonstrate a newly developed standalone GW software, GWmodelS using a community-level house price data set for Wuhan, China. In detail, a number of fundamental GW models are illustrated, including GW descriptive statistics, basic and multiscale GW regression, and GW principle component analysis. Additionally, functionality in spatial data management and batch mapping are presented as essential supplementary activities for GW modeling. The software provides significant advantages in terms of a user-friendly graphical user interface, operational efficiency, and accessibility, which facilitate its usage for users from a wide range of domains.

MINUTIA EXTRACTOR AND MINUTIA MATCHER BASED FINGER PRINT RECOGNITION USING DIGITAL IMAGE PROCESSING

The MATLAB code presents a user-friendly graphical interface for fingerprint recognition and verification. Through this interface, users can load fingerprint images and execute a series of image processing tasks aimed at enhancing image quality and extracting crucial features. Operations such as histogram equalization, FFT enhancement, binarization, and thinning are available to optimize the fingerprint images for analysis. The system facilitates minutia extraction, identifying significant points like ridge endings and bifurcations, which are fundamental for fingerprint matching. Moreover, it includes functionalities for removing spurious minutiae, thereby improving the accuracy of the recognition process. Users can save and load fingerprint templates, streamlining the storage and retrieval of extracted features for matching purposes. Additionally, the system supports fingerprint matching, allowing users to compare templates and determine their similarity or match percentage. This versatile tool finds applications in biometric authentication systems, forensic analysis, and security domains, where robust fingerprint recognition and verification are imperative. Overall, the MATLAB code provides a comprehensive framework encapsulated within a user-friendly interface, offering researchers and practitioners a valuable resource for fingerprint analysis and biometric authentication. Keywords : Fingerprint recognition, GUI (Graphical User Interface), Security systems, Access control, Forensic analysis, User-friendly

Hyperfidelis: A Software Toolkit to Empower Precision Agriculture with GeoAI

The potential of artificial intelligence (AI) and machine learning (ML) in agriculture for improving crop yields and reducing the use of water, fertilizers, and pesticides remains a challenge. The goal of this work was to introduce Hyperfidelis, a geospatial software package that provides a comprehensive workflow that includes imagery visualization, feature extraction, zonal statistics, and modeling of key agricultural traits including chlorophyll content, yield, and leaf area index in a ML framework that can be used to improve food security. The platform combines a user-friendly graphical user interface with cutting-edge machine learning techniques, bridging the gap between plant science, agronomy, remote sensing, and data science without requiring users to possess any coding knowledge. Hyperfidelis offers several data engineering and machine learning algorithms that can be employed without scripting, which will prove essential in the plant science community.

Raman Match: Application for Automated Identification of Minerals from Raman Spectroscopy Data

Abstract Raman spectroscopy is a rapid, nondestructive analysis technique that is used in various scientific disciplines, including chemistry, materials science, and biology. The analysis of Raman spectra and the identification of specific substances in unknown samples can be complex and time-consuming tasks due to the large database of Raman spectra. The Raman Match application was developed to simplify and automate the sample identification process through a search and match method. The application integrates the well-established RRUFF Raman database with the Python programming language and provides a user-friendly graphical interface to load Raman spectra, identify and fit peaks, match peaks to the reference libraries, visualize the results, and generate publication-ready figures. The application offers a swift and automated method for mineral identification using Raman spectroscopy in both laboratory and field settings, as well as during planetary exploration missions to extraterrestrial environments with constraints on time and resources.

Metaheuristic optimization based- ensemble learners for the carbonation assessment of recycled aggregate concrete

This study addresses the enhanced prevalence of carbonation, a process accelerating steel reinforcement corrosion in recycled aggregate concrete (RAC) compared to natural aggregate concrete. Traditional carbonation depth assessment methods in RAC are noted for being labor-intensive, costly, and requiring specialized expertise. There is a noted deficiency in the application of machine learning techniques for accurately predicting carbonation depth in RAC, a gap this study aims to fill. Utilizing the extreme gradient boosting (XGBoost) technique, recognized for its efficacy in ensemble machine learning, this study innovates in modeling carbonation depth in RAC. It emphasizes the criticality of hyperparameter optimization of the XGBoost algorithm for maximizing model accuracy. To achieve this, three novel metaheuristic optimization algorithms, including reptile search algorithm (RSA), Aquila optimizer (AO), and arithmetic optimization algorithm (AOA), were introduced as global optimizers for tunning the XGBoost hyperparameters. The study was underpinned by a comprehensive database compiled from extensive literature, facilitating the development of an accurate RAC carbonation depth model. Through rigorous evaluations, including sensitivity analyses, the Wilcoxon signed-rank test, and runtime comparisons, the synthesized models demonstrated exceptional accuracy, with coefficients of determination exceeding 0.95. The XGBoost-AO algorithm, in particular, showcased superior performance, with the XGBoost-RSA algorithm providing efficient predictions considering runtime. SHapley Additive exPlanations (SHAP) interpretation highlighted environmental conditions as significant carbonation depth influencers. A user-friendly graphical user interface was developed, enhancing the practical utility of the findings for predicting carbonation depth progression in RAC over time. This research significantly advances the predictive accuracy for carbonation depth in RAC, contributing to the sustainable management of concrete infrastructures and emphasizing the integration of advanced machine learning techniques with metaheuristic optimization for environmental and structural engineering advancements.

Predicting the web crippling capacity of cold-formed steel lipped channels using hybrid machine learning techniques

Cold-Formed Steel Lipped (CFSL) channels are susceptible to a localized failure mechanism known as web crippling, triggered by concentrated loads or reactions applied to the web of the section. These loads induce buckling and distortion in the web, ultimately leading to the member's collapse. It is a challenging task to accurately determine the web crippling capacity of a CFSL channel due to its complexity and various influencing factors. This paper presents hybrid soft computing techniques for accurately predicting the web crippling capacity of CFSL channels subjected to two flange load cases. The developed soft computing techniques combine Artificial Neural Networks (ANN) with either Genetic Algorithms (GA) or Particle Swarm Optimization (PSO) to improve computational efficiency and accuracy. The finite element models of CFSL channels are developed and validated by experimental results and then employed to generate a database, which is used to train machine learning models, including ANN, GA-ANN, and PSO-ANN. Analysis is undertaken on the reliability of existing design formulas for determining the web crippling capacity of CFSL channels. It is shown that the PSO-ANN model outperforms the other models in terms of prediction accuracy. The existing design codes and formulas are not reliable in estimating the web crippling capacity of CFSL channels. However, the proposed model yields good correlation with finite element analysis results. A user- friendly graphical interface tool is developed for the practical design of cold-formed steel lipped channels.

A Developed Graphical User Interface-Based on Different Generative Pre-trained Transformers Models

Objective: The article investigates the integration of advanced Generative Pretrained Transformers (GPT) models into a user-friendly Graphical User Interface (GUI). The primary objective of this work is to simplify access to complex Natural Language Processing (NLP) tasks for a diverse range of users, including those with limited technical background.
 Methods: The development process of the GUI was comprehensive and systematic:
 • Needs Assessment: This stage involved understanding the requirements and expectations of potential users to ensure the GUI effectively addresses their needs.
 • Preliminary Design and Development: The initial designs were created and developed into a functional GUI, emphasizing the integration of features supporting various NLP tasks like text summarization, translation, and question-answering.
 • Iterative Refinement: Continuous improvements were made based on user feedback, focusing on enhancing user experience, ease of navigation, and customization capabilities.
 Results: The developed GUI successfully integrated GPT models, including GPT-4 Turbo and GPT-3.5, resulting in an intuitive and adaptable interface. It demonstrated efficiency in performing various NLP tasks, thereby making these advanced language processing tools accessible to a broader audience. The GUI's design, emphasizing user-friendliness and adaptability, was particularly noted for its ability to cater to both technical and non-technical users.
 Conclusion: In conclusion, the article illustrates the significant impact of combining advanced GPT models with a Graphical User Interface to democratize the use of NLP tools. This integration not only makes complex language processing more accessible but also marks a pivotal step in the inclusive application of AI technology across various domains. The successful implementation of the GUI highlights the potential of AI in enhancing user interaction and broadening the scope of technology usage in everyday tasks.

ViNe-Seg: deep-learning-assisted segmentation of visible neurons and subsequent analysis embedded in a graphical user interface.

Segmentation of neural somata is a crucial and usually the most time-consuming step in the analysis of optical functional imaging of neuronal microcircuits. In recent years, multiple auto-segmentation tools have been developed to improve the speed and consistency of the segmentation process, mostly, using deep learning approaches. Current segmentation tools, while advanced, still encounter challenges in producing accurate segmentation results, especially in datasets with a low signal-to-noise ratio. This has led to a reliance on manual segmentation techniques. However, manual methods, while customized to specific laboratory protocols, can introduce variability due to individual differences in interpretation, potentially affecting dataset consistency across studies. In response to this challenge, we present ViNe-Seg: a deep-learning-based semi-automatic segmentation tool that offers (i) detection of visible neurons, irrespective of their activity status; (ii) the ability to perform segmentation during an ongoing experiment; (iii) a user-friendly graphical interface that facilitates expert supervision, ensuring precise identification of Regions of Interest; (iv) an array of segmentation models with the option of training custom models and sharing them with the community; and (v) seamless integration of subsequent analysis steps. ViNe-Seg code and documentation are publicly available at https://github.com/NiRuff/ViNe-Seg and can be installed from https://pypi.org/project/ViNeSeg/.

HemoDL: Hemolytic peptides prediction by double ensemble engines from Rich sequence-derived and transformer-enhanced information

Hemolytic peptides can trigger hemolysis by rupturing red blood cells’ membranes and triggering cell disruption. Due to the labor-intensive and time-consuming in-lab identification process, accurate, high-throughput hemolytic peptide prediction is crucial for the growth of peptide sequence data in proteomics and peptidomics. In this study, we offer the HemoDL ensemble learning model, which learns the distinct distribution of sequence characteristics for predicting the hemolytic activity of peptides using a double LightGBM framework. To determine the most informative encoding features, we compare 17 widely used features across four benchmark datasets. Our investigation reveals that CTD, BPF, Charge, AAC, GDPC, ATC, QSO, and transformer-based features exhibit more positive contributions to detecting the hemolytic activity of peptides. Comparison with eight state-of-the-art methods demonstrates that HemoDL outperforms other models, attaining higher Matthews Correlation Coefficient values on four test datasets, ranging from 6.30% to 16.04%, 6.63%–11.26%, 4.76%–9.92%, and 7.41%–15.03%, respectively. Additionally, we provide the HemoDL with a user-friendly graphical interface available at https://github.com/abcair/HemoDL. In summary, the HemoDL model, leveraging CTD, BPF, Charge, AAC, GDPC, ATC, QSO and transformer-based encoding features within a double LightGBM learning framework, achieves high accuracy in predicting the hemolytic activity of peptides.

Magnetoencephalography Atlas Viewer for Dipole Localization and Viewing.

Magnetoencephalography (MEG) is a noninvasive neuroimaging technique widely recognized for epilepsy and tumor mapping. MEG clinical reporting requires a multidisciplinary team, including expert input regarding each dipole's anatomic localization. Here, we introduce a novel tool, the "Magnetoencephalography Atlas Viewer" (MAV), which streamlines this anatomical analysis. The MAV normalizes the patient's Magnetic Resonance Imaging (MRI) to the Montreal Neurological Institute (MNI) space, reverse-normalizes MNI atlases to the native MRI, identifies MEG dipole files, and matches dipoles' coordinates to their spatial location in atlas files. It offers a user-friendly and interactive graphical user interface (GUI) for displaying individual dipoles, groups, coordinates, anatomical labels, and a tri-planar MRI view of the patient with dipole overlays. It evaluated over 273 dipoles obtained in clinical epilepsy subjects. Consensus-based ground truth was established by three neuroradiologists, with a minimum agreement threshold of two. The concordance between the ground truth and MAV labeling ranged from 79% to 84%, depending on the normalization method. Higher concordance rates were observed in subjects with minimal or no structural abnormalities on the MRI, ranging from 80% to 90%. The MAV provides a straightforward MEG dipole anatomic localization method, allowing a nonspecialist to prepopulate a report, thereby facilitating and reducing the time of clinical reporting.

Online and desktop graphical user interfaces for xtb programme from atomistica.online platform

ABSTRACT In this paper, we introduce our development of both online and desktop graphical user interfaces (GUIs) for the xtb programme to enhance accessibility and user-friendliness. The field of molecular modelling has been profoundly transformed by the advent of modern semiempirical calculations, with the xtb programme from Prof. Stefan Grimme's group emerging as an exceptionally powerful tool in this domain. xtb programme operates through a command prompt interface, necessitating a certain level of technical proficiency from its users. The need to use a command prompt might be repealing for some users, such as beginners or users who are not dedicated to computational techniques. This was the primary motivation to develop GUIs for this programme, considering that experienced molecular modellers would also appreciate a user-friendly GUI application that helps them automate their modelling tasks. The online GUI represents a significant leap forward, allowing users to execute xtb calculations directly from their web browsers. Complementing the online version, our desktop GUI extends the functionalities available to users, harnessing the more extensive capabilities of the xtb programme. As all other tools of atomistica.online project, xtb GUIs are freely available at the project's official website – https://atomistica.online.

AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings

BackgroundPlant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.ResultsHere, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity.ConclusionsAraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.

NeuronBridge: an intuitive web application for neuronal morphology search across large data sets

BackgroundNeuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome’s structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities.ResultsHere, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine.ConclusionsNeuronBridge fills a critical gap in the Drosophila research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at http://neuronbridge.janelia.org.

X-Ray Calc 3: improved software for simulation and inverse problem solving for X-ray reflectivity.

This work introduces X-Ray Calc (XRC), an open-source software package designed to simulate X-ray reflectivity (XRR) and address the inverse problem of reconstructing film structures on the basis of measured XRR curves. XRC features a user-friendly graphical interface that facilitates interactive simulation and reconstruction. The software employs a recursive approach based on the Fresnel equations to calculate XRR and incorporates specialized tools for modeling periodic multilayer structures. This article presents the latest version of the X-Ray Calc software (XRC3), with notable improvements. These enhancements encompass an automatic fitting capability for XRR curves utilizing a modified flight particle swarm optimization algorithm. A novel cost function was also developed specifically for fitting XRR curves of periodic structures. Furthermore, the overall user experience has been enhanced by developing a new single-window interface.

Prediction of non-uniform shrinkage of steel-concrete composite slabs based on explainable ensemble machine learning model

The non-uniform shrinkage (NUS) of steel-concrete composite slabs has been proved to significantly affect the long-term performance of composite slabs. But the reliable analysis method has not yet been proposed due to the multi-factor coupling mechanism of material composition and boundary conditions. A database containing 782 samples was established in this paper, and the index set containing 5 feature parameters was selected by analyzing the physical mechanism of NUS of steel-concrete composite slabs. The predictive capabilities of 8 machine learning (ML) models with different working mechanisms were compared, and the reliability of ML models was verified by Taylor and box scatter from the perspective of global and single sample, respectively. Furthermore, sensitivity analysis was conducted using the SHapley Additive exPlanations (SHAP) method to evaluate the impact of each parameter on the NUS of steel-concrete composite slabs. Finally, a user-friendly graphical user interface (GUI) was developed to guide engineering design. The limitations and potential improvements of the ML models established in this paper were discussed. The results indicate that the variance inflation factor (VIF) method and correlation analysis can effectively mitigate the influence of data multicollinearity and variable collinearity, and the pearson coefficient and VIF value of the input variables are less than 0.35 and 5. Gradient boosting decision tree (GBDT) has the best prediction performance, with R2 of 0.933 and 0.927 on the training set and testing set, respectively. Relative distance (x) and age (t) are the key parameters affecting the NUS of steel-concrete composite slabs. The procedure and GUI developed in this paper can effectively guide the intelligent prediction of the composite slab NUS.

ScribbleCDNet: Change detection on high-resolution remote sensing imagery with scribble interaction

Change detection on high-resolution remote sensing imagery using end-to-end deep learning methods has attracted considerable attention in recent years. Nevertheless, the performance of end-to-end models on complicated scenarios still is limited. Interactive deep-learning models have proven to be a valuable technique for enhancing model performance with minimal human interaction. For instance, the clicks-based interactive models have attracted much attention recently, however, their performance on large regions or complex areas still can be further improved, because they cannot provide accurate semantics or shape prior information of the change regions for the interactive models, as we know that the shape and semantic features of changed regions in remote sensing imagery are typically irregular and complex. Scribble-based interactive form, which can accurately represent the shape or semantic features of the changed regions, thus it is quite suitable for change detection tasks in remote sensing imagery. Therefore, we proposed a novel interactive deep learning model called ScribbleCDNet in this manuscript, which pioneered the use of scribble as an interactive form for detecting change in bi-temporal high-resolution remote sensing imageries. Compared with the widely used clicks-based interactive deep learning models, the proposed ScribbleCDNet acquired superior results on four open-sourced change detection datasets. Last but not least, we also developed an interactive change detection tool with a user-friendly graphical interface, and it can aid researchers in conducting change detection or generating training samples conveniently. Moreover, the proposed ScribbleCDNet can also inspire researchers to develop other interactive deep-learning models related to semantic segmentation, landcover classification, or object extraction in high-resolution remote sensing imageries.

An improved approach for early diagnosis of Parkinson’s disease using advanced DL models and image alignment

An innovative approach to enhance image alignment through affine transformation, allowing images to be rotated from 0 to 135 degrees. This transformation is a crucial step in improving the diagnostic process, as image misalignment can lead to inaccurate results. The accurate alignment sets the stage for a robust U-Net model, which excels in image segmentation. Precise segmentation is vital for isolating affected brain regions, aiding in the identification of PD-related anomalies. Finally, we introduce the DenseNet architecture model for disease classification, distinguishing between PD and non-PD cases. The combination of these DL models outperforms existing diagnostic approaches in terms of acceptance precision (99.45%), accuracy (99.95%), sensitivity (99.67%), and F1-score (99.84%). In addition, we have developed user-friendly graphical interface software that enables efficient and reasonably accurate class detection via Magnetic Resonance Imaging (MRI). This software exhibits superior efficiency contrasted to current cutting-edges technique, presenting an encouraging opportunity for early disease detection. In summary, our research tackles the problem of low accuracy in existing PD diagnostic models and addresses the critical need for more precise and timely PD diagnoses. By enhancing image alignment and employing advanced DL models, we have achieved substantial improvements in diagnostic accuracy and provided a valuable tool for early PD detection.

PolyWeight: A free and open-source program for determination of molecular weight distribution of linear polymers

This paper introduces PolyWeight, a Python software featuring a user-friendly graphical user interface (GUI), which offers two distinct approaches for MWD determination: an analytical relation-based method and a parametric model-based method. By utilizing dynamic moduli, users can calculate MWD as well as molecular weight averages such as Mn, Mw, and Mz. The functionality of PolyWeight is validated using synthetic data and real data obtained from the literature, exhibiting good agreement. Log files and datasets used in this work are available on GitHub. Program summaryProgram Title: PolyWeightCPC Library link to program files:https://doi.org/10.17632/rn8t4mmvyd.1Developer's repository link:https://github.com/a-minotto/PolyWeightLicensing provisions: GPLv3Programming language: Python 3.xNature of problem: Analytical rheology enables the determination of molecular weight distribution (MWD) by solving inverse problems. The reptation theory establishes a liaison between the viscoelastic behavior and MWD, permitting the application of mathematical resolution methods like regularization and parameterization. PolyWeight offers two solutions: a parameterization-based method that assumes a generalized exponential distribution (GEX) for the MWD, and an analytical relationship-based method that utilizes the relaxation spectrum.Solution method: The analytical relationship-based solution incorporates a mathematical formulation that considers the relaxation spectrum obtained from the dynamic moduli using the NLREG software. The numerical integration of the relaxation spectrum is performed utilizing the integrate.quad function from the SciPy library. An optimization process is employed in the parametric solution to perform a multiobjective fit of the viscoelastic models to the dynamic moduli. The integrals in this case are also calculated using the integrate.quad package from the SciPy library. The optimization procedure utilizes the lmfit library, where a Minimizer object is created based on the optimization function and subsequently utilized with the minimize function, employing the default Levenberg-Marquardt algorithm for the fit.

Advance optimum design of multi-layer compound cylinders

The purpose of this work is to find the optimum design for two-layer compound cylinders of the same material with an open end condition by using the elimination method. The optimization method depends on reducing the number of design variables with a simultaneous yield hypothesis for all layers of the compound cylinder. A combination of von Mises and Tresca criteria is used as a yield criterion, and the superposition principle is used to evaluate the equivalent stresses for each cylinder. By considering the working pressure and the internal diameter of the inner cylinder as the design parameters (constants during optimization) and the total thickness of the compound cylinder as the objective function, the optimization algorithm has been programmed in C# with a user-friendly graphical interface. The optimization results (outer diameter, interference diameter, and shrinkage pressure) are obtained for different working pressures and compared with the optimum design, which is based only on the Tresca criterion. The total mass of the compound cylinder can be reduced by up to 50% by using the von Mises–Tresca combination criterion. The optimized results are validated numerically by using finite element analysis in the ANSYS Workbench. The theoretical result and the FEA result agree with each other with errors of about 2%. The behavior of the optimized parameters for different working pressures is also observed and presented.

OralImmunoAnalyser: a software tool for immunohistochemical assessment of oral leukoplakia using image segmentation and classification models.

Oral cancer ranks sixteenth amongst types of cancer by number of deaths. Many oral cancers are developed from potentially malignant disorders such as oral leukoplakia, whose most frequent predictor is the presence of epithelial dysplasia. Immunohistochemical staining using cell proliferation biomarkers such as ki67 is a complementary technique to improve the diagnosis and prognosis of oral leukoplakia. The cell counting of these images was traditionally done manually, which is time-consuming and not very reproducible due to intra- and inter-observer variability. The software presently available is not suitable for this task. This article presents the OralImmunoAnalyser software (registered by the University of Santiago de Compostela-USC), which combines automatic image processing with a friendly graphical user interface that allows investigators to oversee and easily correct the automatically recognized cells before quantification. OralImmunoAnalyser is able to count the number of cells in three staining levels and each epithelial layer. Operating in the daily work of the Odontology Faculty, it registered a sensitivity of 64.4% and specificity of 93% for automatic cell detection, with an accuracy of 79.8% for cell classification. Although expert supervision is needed before quantification, OIA reduces the expert analysis time by 56.5% compared to manual counting, avoiding mistakes because the user can check the cells counted. Hence, the SUS questionnaire reported a mean score of 80.9, which means that the system was perceived from good to excellent. OralImmunoAnalyser is accurate, trustworthy, and easy to use in daily practice in biomedical labs. The software, for Windows and Linux, with the images used in this study, can be downloaded from https://citius.usc.es/transferencia/software/oralimmunoanalyser for research purposes upon acceptance.