Diagnosis System Research Articles

Trapezoidal neutrosophic teaching learning-based optimization in enhancing accuracy of diabetes prognosis

Diabetes is one of chronic diseases in which blood glucose (sugar) level soar up high where human body are incapable to absorb it properly. It is important to have an appropriate diagnosis for proper management and treatment. The aim of this manuscript is to provide a more accurate diabetes prediction model through the new adaptive Trapezoidal Neutrosophic Teaching Learning-Based Optimization (TLBO) method. In order to address the inherent uncertainties and imprecisions in medical data, the suggested model makes use of the resilience of Trapezoidal Neutrosophic sets. The Trapezoidal Neutrosophic set theory provides a suitable basis for developing rule/knowledge-based systems in the medical field. The present investigation makes use of the dataset acquired from the Pima Indians Diabetes Database (PIDD) website, which has an extensive global collection of diabetes datasets. The performance of our model is evaluated against several existing methodologies, including Intuitionistic Neuro-Fuzzy System (INFS) Structure, Fuzzy Logic based Diabetes Diagnosis System (FLDDS), Fuzzy Verdict Mechanism (FVM) for Diabetes Decision, (Fuzzy Expert System) FES, and Hierarchical Neuro-Fuzzy Binary space partitioning System (HNFB-1). Quantitative analysis validates that proposed methodology achieves an exceptional predictive accuracy of 99.89 %, which is substantially higher than the comparative methodologies, namely INFS Structure (88.76 %), FLDDS (87.2 %), FVM for Diabetes Decision (85.03 %), FES (81.7 %), and HNFB-1 (78.26 %). These enhancements demonstrate show how well the suggested model works to lower diagnostic errors and increase dependability.

Sound Classification for Non-Destructive Diagnosis of Basal Stem Rot Disease based on Stem Density in Oil Palm Trunks

Oil palm trees play a crucial role in the economies of Southeast Asian countries, providing palm oil that is essential for various products and consumption. However, the Basal Stem Rot (BSR) disease caused by the white-rot fungus Ganoderma spp. poses a significant challenge, leading to wilting and death of the trees. This study proposes a practical and accessible approach for the routine detection of Basal Stem Rot (BSR) disease in oil palm trees caused by Ganoderma spp. A prototype system for initial disease diagnosis is developed by utilizing the local wisdom technique of tapping and assessing knocking sounds based on stem density in oil palm trunks. Three algorithms, Convolutional Neural Network (CNN), Support Vector Machine Classifier (SVC), and Multi-Layer Perceptron (MLP) models are compared for accuracy in two sound recognition experiments. The first experiment involves a three-class classification model (healthy, boundary, and infected areas), while the second excludes the boundary area. Results demonstrate that the CNN model outperforms SVC and MLP, achieving the highest accuracy of 90.73% in the two-class scenario and 84.97% in the three-class scenario, along with superior precision, recall, and F1 score. The CNN model proves to be the most effective in accurately classifying the data in both scenarios compared to the other models. This prototype system offers promising implications for the early detection and proactive management of Ganoderma spp. disease, contributing to the preservation and sustainability of oil palm plantations in Southeast Asia.

A digital twin system for centrifugal pump fault diagnosis driven by transfer learning based on graph convolutional neural networks

In industrial sectors such as shipping, chemical processing, and energy production, centrifugal pumps often experience failures due to harsh operational environments, making it challenging to accurately identify fault types. Traditional fault diagnosis methods, which heavily rely on existing fault datasets, suffer from limited generalization capabilities, especially when substantial labeled and specific fault sample data are lacking. This paper proposes a novel fault diagnosis approach for centrifugal pumps, utilizing a digital twin (DT) framework powered by a graph transfer learning model to address this issue. Firstly, a high-fidelity DT model is constructed to simulate the flow-induced vibration response of the impeller under different health states to enrich the type and scale of the dataset. Secondly, a graph convolutional neural networks (GCN) model is constructed to learn the knowledge of simulation data, and the Wasserstein distance between simulation data and measured data is optimized for adversarial domain adaptation, thereby achieving efficient cross-domain fault diagnosis. Experimental results demonstrate that the proposed algorithm delivers effective fault diagnosis with minimal prior knowledge and outperforms comparable models. Furthermore, the DT system developed using the proposed model enhances the operational reliability of centrifugal pumps, reduces maintenance costs, and presents an innovative application of DT technology in industrial fault diagnosis.

Adaptive Top-K Algorithm for Medical Conversational Diagnostic Model.

With advancements in computing technology and the rapid progress of data science, machine learning has been widely applied in various fields, showing great potential, especially in digital healthcare. In recent years, conversational diagnostic systems have been used to predict diseases through symptom checking. Early systems predicted the likelihood of a single disease by minimizing the number of questions asked. However, doctors typically perform differential diagnoses in real medical practice, considering multiple possible diseases to address diagnostic uncertainty. This requires systems to ask more critical questions to improve diagnostic accuracy. Nevertheless, such systems in acute medical situations need to process information quickly and accurately, but the complexity of differential diagnosis increases the system's computational cost. To improve the efficiency and accuracy of telemedicine diagnostic systems, this study developed an optimized algorithm for the Top-K algorithm. This algorithm dynamically adjusts the number of the most likely diseases and symptoms by real-time monitoring of case progress, optimizing the diagnostic process, enhancing accuracy (99.81%), and increasing the exclusion rate of severe pathologies. Additionally, the Top-K algorithm optimizes the diagnostic model through a policy network loss function, effectively reducing the number of symptoms and diseases processed and improving the system's response speed by 1.3-1.9 times compared to the state-of-the-art differential diagnosis systems.

A Hybrid Approach for Thyroid Disease and Classification Using Attribute Associations Rule Mining

In recent years, thyroid diseases have become increasingly prevalent worldwide. In India, for instance, one in eight women is affected by hypothyroidism, hyperthyroidism, or thyroid cancer. Research indicates that approximately 20% of Indians are diagnosed with endemic goiter. Several factors influence thyroid function, including stress, infection, trauma, toxins, low-calorie diet, and certain medications. Preventing such diseases is crucial as treatments often involve long-term medication or surgical intervention. Thyroid disease diagnosis is pivotal in medicine, where precise classification is essential for prompt treatment. This paper proposes a novel approach to thyroid disease classification by integrating Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) with Association Rule Mining (ARM). The hybridization of PSO and ACO enhances exploration and exploitation capabilities, thereby improving the efficiency of association rule mining for feature selection. The proposed method aims to identify significant association rules among thyroid disease attributes, thereby facilitating accurate classification. Experimental evaluations conducted on benchmark thyroid disease datasets demonstrate the effectiveness of the proposed approach in terms of classification accuracy and computational efficiency. The results indicate that the hybrid PSO and ACO-based association rule mining approach surpasses traditional methods, underscoring its potential to enhance thyroid disease diagnosis systems.

RA-XTNet: A Novel CNN Model to Predict Rheumatoid Arthritis from Hand Radiographs and Thermal Images: A Comparison with CNN Transformer and Quantum Computing.

The aim and objective of the research are to develop an automated diagnosis system for the prediction of rheumatoid arthritis (RA) based on artificial intelligence (AI) and quantum computing for hand radiographs and thermal images. The hand radiographs and thermal images were segmented using a UNet++ model and color-based k-means clustering technique, respectively. The attributes from the segmented regions were generated using the Speeded-Up Robust Features (SURF) feature extractor and classification was performed using k-star and Hoeffding classifiers. For the ground truth and the predicted test image, the study utilizing UNet++ segmentation achieved a pixel-wise accuracy of 98.75%, an intersection over union (IoU) of 0.87, and a dice coefficient of 0.86, indicating a high level of similarity. The custom RA-X-ray thermal imaging (XTNet) surpassed all the models for the detection of RA with a classification accuracy of 90% and 93% for X-ray and thermal imaging modalities, respectively. Furthermore, the study employed quantum support vector machine (QSVM) as a quantum computing approach which yielded an accuracy of 93.75% and 87.5% for the detection of RA from hand X-ray and thermal images. In addition, vision transformer (ViT) was employed to classify RA which obtained an accuracy of 80% for hand X-rays and 90% for thermal images. Thus, depending on the performance measures, the RA-XTNet model can be used as an effective automated diagnostic method to diagnose RA accurately and rapidly in hand radiographs and thermal images.

A Hybrid Framework for Plant Leaf Region Segmentation: Comparative Analysis of Swarm Intelligence with Convolutional Neural Networks

Agriculture is important for the survival of humanity since about 70% of the world's population is engaged in agricultural pursuits to varying degrees. The previous and present methodology lacks ways to identify diseases in different crops within an agricultural environment. The crops most impacted by these illnesses are tomatoes, leading to a significant increase in the price of tomatoes. Controlling tomato illnesses is crucial for optimal development and production, making early diagnosis and disease detection vital. Early disease diagnosis and treatment in tomato plants can lead to improved yields. Tomato plants are susceptible to illnesses, which can significantly affect the quality, quantity, and productivity of the crop if not properly treated. Despite many unsuccessful efforts to utilize machine learning methods for detecting and classifying illnesses in tomato plants. This study introduces a comparative framework for segmenting tomato leaf regions using both conventional and swarm intelligence methodologies to determine the more effective approach. This framework enables the development of a tomato plant diagnosis system capable of analyzing various sorts of images, including both standard and custom image datasets. The obtained evaluation parameters of the proposed model in terms of average precision, recall, f-measure, error, and accuracy are 0.914, 0.915, 0.915, 1.55% and 98.45%, respectively. We performed a comparative examination of six scenarios: T-model, K-model, TPSO-model, KPSO-model, TGO-model, and KGO-model. The combination of K-means with the GO algorithm proved to be a powerful hybrid technique, with an accuracy of 96.45%. Comparatively, the T-model, K-model, TPSO-model, KPSO-model, and TGO-model attained accuracies of 85.73%, 86.61%, 87.54%, 88.64%, and 92.21% correspondingly.

Optimal segmentation of non-linear and non-stationary time series based on fractal dimension and Poincare section and its application in solving EEG-signal

Non-linearity and non-stationarity characteristics in physiological and biological time series limit the reliability of conventional analysis. The main challenge in pattern recognition problems is determining the Optimal-Window-Duration (OWD) for segmenting these time series. Our study presents an innovative algorithm, based on Individual-Optimal-Segmentation (IOS), which integrates chaos theory using fractal dimension, and Poincare section into the estimation of the OWD for segmenting time series with non-linear and non-stationary characteristics. In the logistic map case study, the IOS algorithm proved to be effective in detecting cycles in a range of numerical examples, from period-2 to chaotic behavior. So that its performance was superior to previous approaches, especially when it comes to detecting chaotic behaviors. Furthermore, the combination of the IOS algorithm with the Welch signal processing method shows promise in identifying abnormal Power-Spectral-Density (PSD) curves related to Beta and Gamma rhythms in depression diagnosis scenarios. The generated depression diagnosis system attained an accuracy (%) of 99.07±0.35 (with 2-channel) using the Monte Carlo simulation method. These findings suggest a potentially valuable tool for addressing segmentation challenges in time series analysis, particularly in contexts involving complex, dynamic data. While, it is suggested that future research investigate the computational efficiency and robustness of the IOS algorithm when handling large-scale or real-time data and increase its application in various fields in data science and signal processing. Furthermore, expanding the application range of the IOS algorithm for other pattern recognition problems with physiological or biological data can strengthen the generalizability and practical application of the algorithm.

Application of Synchronous Evaluation-Diagnosis Model with Quantitative Stressor-Response Analysis (SED-QSR) to Urban Lake Ecological Status: A Proposed Multiple-Level System.

Ecological integrity assessment and degradation diagnosis are used globally to evaluate the health of water bodies and pinpoint critical stressors. However, current studies mainly focus on separate evaluation or diagnosis, leading to an inadequate exploration of the relationship between stressors and responses. Here, based on multiple data sets in an urban lake system, a synchronous evaluation-diagnosis model with quantitative stressor-response analysis was advanced, aiming to improve the accuracy of evaluation and diagnosis. The weights for key physicochemical stressors were quantitatively determined in the sequence of NDAVIadj > CODMn > TP > NH4+-N by the combination of generalized additive model and structural equation modeling, clarifying the most significant effects of aquatic vegetation on the degradation of fish assemblages. Then, sensitive biological metrics were screened by considering the distinct contributions of four key stressors to alleviate the possible deviation caused by common methods. Finally, ecological integrity was evaluated by summing the key physicochemical stressors and sensitive biological metrics according to the model-deduced weights instead of empirical weights. Our system's diagnosis and evaluation results achieved an accuracy of over 80% when predicting anthropogenic stress and biological status, which highlights the great potential of our multiple-level system for ecosystem management.

Breast lesions segmentation from ultrasound images using DeepLabV3 + model with channel and spatial attention mechanism

Segmenting breast tumors in ultrasound images is pivotal for computer-aided diagnosis (CAD) systems aimed at detecting breast cancer. This research contributes directly to the Sustainable Development Goal (SDG), good health and well-being by leveraging innovative deep learning technique to boost the accuracy of breast tumor segmentation. Achieving accurate segmentation is crucial for precise tumor size, shape, and location determination, which in turn facilitates subsequent tumor quantification and classification. However, this task presents challenges, particularly when dealing with small tumors. The complexity arises from factors such as speckle noise, variations in tumor shapes and sizes across patients, and the presence of tumor-like regions in the images. Although deep learning-based methods have shown remarkable success in biomedical image analysis, the current state-of-the-art approaches still struggle to achieve satisfactory performance in segmenting small breast tumors. In this paper, BUS (Breast Ultrasound) dataset was initially applied on state of-the art DeepLabV3 + architecture for the segmentation of breast lesions in ultrasound images. After that to focus on more informative features, a modified DeepLabV3 + has been proposed by integrating a CBAM module in encoder as well as decoder. At the end, comparative analysis of state-of-art DeepLabV3 + model and proposed modified DeepLabV3 + has also been presented in terms of performance metrics like Dice Coefficient, Intersection over Union, Precision, Recall and Specificity. The proposed model demonstrates outstanding performance across different evaluation metrics achieving precision, recall, specificity, dice coefficient and Intersection over Union (IoU) values of 0.974, 0.933, 0.997, 0.951, and 0.933. respectively.

Application of artificial intelligence in lung cancer screening: A real-world study in a Chinese physical examination population.

With the rapid increase of chest computed tomography (CT) images, the workload faced by radiologists has increased dramatically. It is undeniable that the use of artificial intelligence (AI) image-assisted diagnosis system in clinical treatment is a major trend in medical development. Therefore, in order to explore the value and diagnostic accuracy of the current AI system in clinical application, we aim to compare the detection and differentiation of benign and malignant pulmonary nodules between AI system and physicians, so as to provide a theoretical basis for clinical application. Our study encompassed a cohort of 23 336 patients who underwent chest low-dose spiral CT screening for lung cancer at the Health Management Center of West China Hospital. We conducted a comparative analysis between AI-assisted reading and manual interpretation, focusing on the detection and differentiation of benign and malignant pulmonary nodules. The AI-assisted reading exhibited a significantly higher screening positive rate and probability of diagnosing malignant pulmonary nodules compared with manual interpretation (p < 0.001). Moreover, AI scanning demonstrated a markedly superior detection rate of malignant pulmonary nodules compared with manual scanning (97.2% vs. 86.4%, p < 0.001). Additionally, the lung cancer detection rate was substantially higher in the AI reading group compared with the manual reading group (98.9% vs. 90.3%, p < 0.001). Our findings underscore the superior screening positive rate and lung cancer detection rate achieved through AI-assisted reading compared with manual interpretation. Thus, AI exhibits considerable potential as an adjunctive tool in lung cancer screening within clinical practice settings.

A novel CLIPS-based medical expert system for migraine diagnosis and treatment recommendation

Migraines are classified as a neurological disorder defined by recurrent headaches with pain that ranges from mild to severe. Currently, this disorder lacks a permanent cure and definitive diagnostic test. Diagnosis instead requires an assessment of physical and psychological symptoms which differ among patients. To help in the diagnosis process, medical expert systems have been developed and validated since 1960. In this paper, we propose the Migraine Diagnosis and Treatment Expert System (MDATES), a medical expert system for migraine diagnosis and treatment recommendation. The system was designed and implemented using the C Language Integrated Production System (CLIPS) shell. MDATES is able to recognize seven symptoms, two classes of migraines (chronic and episodic), and four subtypes of migraine-classification knowledge (hormonal, aura, hemiplegic, and cluster). A dataset of 300 anonymized patient records with confirmed migraine cases was used to test the system. The diagnoses generated by MDATES were compared against the known diagnoses, and a high level of accuracy was observed, with 67% of the 100 training cases were correctly diagnosed, and 100% of the 200 testing cases were correctly diagnosed. These results highlight the effectiveness and reliability of MDATES and provide valuable assistance to medical professionals in diagnosing migraines. Moreover, we present a literature review that places our proposed system within the broader context of rule-based expert systems for migraine diagnosis and treatment recommendation. This review explores the effectiveness, limitations, and challenges of these systems, and accurately places our system within the current landscape.

AI-aided cardiovascular disease diagnosis in cattle from retinal images: Machine learning vs. deep learning models

Cardiovascular diseases (CVD) in animals can severely impact the heart and circulatory systems, like those in humans. Early diagnosis and treatment are crucial for improving animal welfare and lifespan. Traditional diagnostic methods face challenges such as insufficient anamnesis information, high costs of biochemical and hematological tests, and increasing data complexity. This study aims to address these issues by developing AI-based diagnostic systems for fast and accurate CVD diagnosis in cattle using retinal images. A total of 1118 retinal images from 100 cattle were collected, with 52 diagnosed with CVD and 48 as non-CVD. The dataset is publicly available on Kaggle. We evaluated three machine learning methods (Extreme Learning Machine, K-Nearest Neighbors, and Support Vector Machine) and four deep learning models (DenseNet201, ResNet101, SqueezeNet, and InceptionV3) for their diagnostic capabilities. ResNet101 emerged as the most effective model, achieving an accuracy of 96.1 ± 3.15 %, sensitivity of 97.3 ± 2.96 %, specificity of 94.9 ± 4.07 %, and an F1-score of 96.4 ± 0.03. This study demonstrates that AI-based systems, particularly deep learning models, can significantly improve the accuracy of CVD diagnosis in animals, marking a critical advancement in veterinary healthcare.

Online tree-based planning for active spacecraft fault estimation and collision avoidance.

Autonomous robots operating in uncertain or hazardous environments subject to state safety constraints must be able to identify and isolate faulty components in a time-optimal manner. When the underlying fault is ambiguous and intertwined with the robot's state estimation, motion plans that discriminate between simultaneous actuator and sensor faults are necessary. However, the coupled fault mode and physical state uncertainty creates a constrained optimization problem that is challenging to solve with existing methods. We combined belief-space tree search, marginalized filtering, and concentration inequalities in our method, safe fault estimation via active sensing tree search (s-FEAST), a planner that actively diagnoses system faults by selecting actions that give the most informative observations while simultaneously enforcing probabilistic state constraints. We justify this approach with theoretical analysis showing s-FEAST's convergence to optimal policies. Using our robotic spacecraft simulator, we experimentally validated s-FEAST by safely and successfully performing fault estimation while on a collision course with a model comet. These results were further validated through extensive numerical simulations demonstrating s-FEAST's performance.

Technological Frontiers in Brain Cancer: A Systematic Review and Meta-Analysis of Hyperspectral Imaging in Computer-Aided Diagnosis Systems.

Brain cancer is a substantial factor in the mortality associated with cancer, presenting difficulties in the timely identification of the disease. The precision of diagnoses is significantly dependent on the proficiency of radiologists and neurologists. Although there is potential for early detection with computer-aided diagnosis (CAD) algorithms, the majority of current research is hindered by its modest sample sizes. This meta-analysis aims to comprehensively assess the diagnostic test accuracy (DTA) of computer-aided design (CAD) models specifically designed for the detection of brain cancer utilizing hyperspectral (HSI) technology. We employ Quadas-2 criteria to choose seven papers and classify the proposed methodologies according to the artificial intelligence method, cancer type, and publication year. In order to evaluate heterogeneity and diagnostic performance, we utilize Deeks' funnel plot, the forest plot, and accuracy charts. The results of our research suggest that there is no notable variation among the investigations. The CAD techniques that have been examined exhibit a notable level of precision in the automated detection of brain cancer. However, the absence of external validation hinders their potential implementation in real-time clinical settings. This highlights the necessity for additional studies in order to authenticate the CAD models for wider clinical applicability.

VTCNet: A Feature Fusion DL Model Based on CNN and ViT for the Classification of Cervical Cells

ABSTRACTCervical cancer is a common malignancy worldwide with high incidence and mortality rates in underdeveloped countries. The Pap smear test, widely used for early detection of cervical cancer, aims to minimize missed diagnoses, which sometimes results in higher false‐positive rates. To enhance manual screening practices, computer‐aided diagnosis (CAD) systems based on machine learning (ML) and deep learning (DL) for classifying cervical Pap cells have been extensively researched. In our study, we introduced a DL‐based method named VTCNet for the task of cervical cell classification. Our approach combines CNN‐SPPF and ViT components, integrating modules like Focus and SeparableC3, to capture more potential information, extract local and global features, and merge them to enhance classification performance. We evaluated our method on the public SIPaKMeD dataset, achieving accuracies, precision, recall, and F1 scores of 97.16%, 97.22%, 97.19%, and 97.18%, respectively. We also conducted additional experiments on the Herlev dataset, where our results outperformed previous methods. The VTCNet method achieved higher classification accuracy than traditional ML or shallow DL models through this integration. Related codes: https://github.com/Camellia‐0892/VTCNet/tree/main.

A Decision Support System For Early Stage Parkinson's Diagnosis from EEG Data Using Symbolic Mutual Information and KAC Features

Parkinson's disease (PD) is a serious neurological disease that is threatening the whole world population. The devolution of the neurons located in the substanstia nigra of the brain causes, bradykinesia, rigidity and resting tremor, which are characteristic motor symptoms, occuring in advanced stages. Currently, there is not an effective treatment for PD, it is just controlled by some prescribtions. Early detection of this disease affects the choice of treatment. Recent studies on early diagnosis by analyzing electroencephalography (EEG) recordings have provided a glimmer of hope. Therefore, in this study, an efficient PD detection method from EEG data by using a new set of features is searched. An opensource resting state data of 28 subjects divided as Parkinson and control gorups were anlyzed. PSDs of the EEG frequency bands that are delta, theta, alpha, beta and gamma and Median Spectral Frequency (MSF), Spectral Entropy (SE), Kolmogorov Algorithmic Complexity (KAC) and Weighted Symbolic Mutual Information (wSMI) were extracted as features. The performance of the PD and control group was evaluated with Gradient Boosting (GB), Gaussian Naive Bayes (GNB), and K-nearest Neighbor (KNN), Support Vector Machines (SVM), Logistic Regression (LR), Categorical Boosting (CatBoost) and Extreme Gradient Boosting (XGBoost) Algorithms. A 85% accuracy was achieved with the XGBoost algorithm, using 31 channels and 13 features which outperforms the results of previous studies using this dataset in the literature.

A New Approach for Effective Retrieval of Medical Images: A Step towards Computer-Assisted Diagnosis.

The biomedical imaging field has grown enormously in the past decade. In the era of digitization, the demand for computer-assisted diagnosis is increasing day by day. The COVID-19 pandemic further emphasized how retrieving meaningful information from medical repositories can aid in improving the quality of patient's diagnosis. Therefore, content-based retrieval of medical images has a very prominent role in fulfilling our ultimate goal of developing automated computer-assisted diagnosis systems. Therefore, this paper presents a content-based medical image retrieval system that extracts multi-resolution, noise-resistant, rotation-invariant texture features in the form of a novel pattern descriptor, i.e., MsNrRiTxP, from medical images. In the proposed approach, the input medical image is initially decomposed into three neutrosophic images on its transformation into the neutrosophic domain. Afterwards, three distinct pattern descriptors, i.e., MsTrP, NrTxP, and RiTxP, are derived at multiple scales from the three neutrosophic images. The proposed MsNrRiTxP pattern descriptor is obtained by scale-wise concatenation of the joint histograms of MsTrP×RiTxP and NrTxP×RiTxP. To demonstrate the efficacy of the proposed system, medical images of different modalities, i.e., CT and MRI, from four test datasets are considered in our experimental setup. The retrieval performance of the proposed approach is exhaustively compared with several existing, recent, and state-of-the-art local binary pattern-based variants. The retrieval rates obtained by the proposed approach for the noise-free and noisy variants of the test datasets are observed to be substantially higher than the compared ones.

A computer-aided diagnosis system for brain tumors in magnetic resonance imaging (MRI)

A computer-aided diagnosis system for brain tumors in magnetic resonance imaging (MRI)

Decision Support Systems for Disease Detection and Diagnosis

The last few years have been characterized by a large amount of research activity in the field of healthcare for both the improvement of diagnostic treatments and the development of simple, efficient, and multi-tasking applications [...]