Laszlo Szilagyi

• PhD habil
• Professor at Obuda University

Background: Infant brain tissue segmentation from MRI data is a critical task in medical imaging, particularly challenging due to the evolving nature of tissue contrasts in the early months of life. The difficulty increases as gray matter (GM) and white matter (WM) intensities converge, making accurate segmentation challenging. This study aims to d...

Detecting microsleep in real time is crucial to facilitating the transition from semi-autonomous systems to completely autonomous driving technologies. Integrating sophisticated detection algorithms with vehicle control systems enables the provision of prompt corrective measures, such as driver alerts or temporary vehicle control. Minimizing the pr...

Background: Brain tumors are highly complex, making their detection and classification a significant challenge in modern medical diagnostics. The accurate segmentation and classification of brain tumors from MRI images are crucial for effective treatment planning. This study aims to develop an advanced neural network architecture that addresses the...

Background/Objectives: Managing blood glucose levels effectively remains a significant challenge for individuals with diabetes. Traditional methods often lack the flexibility needed for personalized care. This study explores the potential of reinforcement learning-based approaches, which mimic human learning and adapt strategies through ongoing int...

Diabetes is a disease when the body can no longer properly regulate blood glucose level, which can lead to life-threatening situations. To avoid such situations and regulate blood glucose level, patients with severe form of diabetes need insulin injections. Ideally, the system should automatically decide when best to inject insulin and how much to...

In recent times, the prevalence of chatbot technology has notably increased, particularly in the realm of medical assistants. However, there is a noticeable absence of medical chatbots that cater to the Hungarian language. Consequently, Hungarian-speaking people currently lack access to an automated system capable of providing assistance with their...

Diabetes mellitus (DM) is a persistent metabolic disorder associated with the hormone insulin. The two main types of DM are type 1 (T1DM) and type 2 (T2DM). Physical activity plays a crucial role in the therapy of diabetes, benefiting both types of patients. The detection, recognition, and subsequent classification of physical activity based on typ...

Artificial intelligence (AI) technologies have significantly advanced the field of medical imaging, revolutionizing diagnostic and therapeutic processes [...]

Predicting burnout from texts by using AI necessitates an interdisciplinary approach, combining sports science, psychology, and machine learning. This research is essential to understanding the psychological signs of burnout and the methods to gauge it, coupled with expertise in NLP and sentiment analysis techniques. Collecting athletes’ textual da...

In the contemporary sports science domain, artificial intelligence (AI) has emerged as a pivotal tool, driving breakthroughs in athlete training and rehabilitation. Personalized training schedules, sculpted through AI algorithms, promise optimized performance and reduced injury likelihood. Wearable technologies, combined with AI-boosted functionali...

Background: Optimal sports performance requires a balance between intensive training and adequate rest. IMUs provide objective, quantifiable data to analyze performance dynamics, despite the challenges in quantifying athlete training loads. The ability of AI to analyze complex datasets brings innovation to the monitoring and optimization of athlete...

The so-called fuzzy-possibilistic c-means (FPCM) algorithm was introduced as an early mixed-partition method aiming to eliminate some adverse effects present in the behavior of the fuzzy c-means (FCM) and the possibilistic c-means (PCM) algorithms. A great advantage of FPCM was the low number of its parameters, as it eliminated the possibilistic pe...

In the realm of multilingual, AI-powered, real-time optical character recognition systems, this research explores the creation of an optimal, vocabulary-based training dataset. This comprehensive endeavor seeks to encompass a range of criteria: comprehensive language representation, high-quality and diverse data, balanced datasets, contextual under...

The paper examines the potential of artificial intelligence (AI) in parsing text and conducting sentiment analysis to identify early markers of mental health and neurodegenerative disorders. Through the analysis of textual data, we investigate whether AI can provide a noninvasive, continuous, and objective complement to traditional diagnostic pract...

sRPE and ACWR are valuable tools for controlling fatigue levels and minimizing injuries in performance sports. Their ability to provide individualized assessment, integrate subjective and objective measures, and inform data-driven decision-making makes them essential components of a comprehensive sports safety and performance monitoring system. The...

Early detection and intervention of injuries, lesions, or brain anomalies can significantly improve athletes recovery process, reducing long-term impact and unexpected health side effects. AI-supported health anomaly detection systems provide high accuracy and consistency in real-time image analysis, out-performing human counterparts, especially in...

Interstitial Lung Diseases (ILDs) represent a heterogeneous group of several rare diseases that are di cult to predict, diagnose and monitor. There are no predictive biomarkers for ILDs, clinical signs are similar to the ones for other lung diseases, the radiological features are not easy to recognize, and require manual radiologist review. Data-dr...

The ability of healthcare workers to learn proper hand hygiene has been an understudied area of research. Generally, hand hygiene skills are regarded as a key contributor to reduce critical infections and healthcare-associated infections. In a clinical setup, at a Neonatal Intensive Care Unit (NICU), the outcome of a multi-modal training initiative...

Background: One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during perfor...

Background: Remote diagnosis using collaborative tools have led to multilingual joint working sessions in various domains, including comprehensive health care, and resulting in more inclusive health care services. One of the main challenges is providing a real-time solution for shared documents and presentations on display to improve the efficacy o...

The automated segmentation of brain tissues and lesions represents a widely investigated research topic. The Brain Tumor Segmentation Challenges (BraTS) organized yearly since 2012 provided standard training and testing data and a unified evaluation framework to the research community, which provoked an intensification in this research field. This...

Non-coordinated physical activity may lead to hypoglycemia, which is a dangerous condition for diabetic people. Decision support systems related to type 1 diabetes mellitus (T1DM) still lack the capability of automated therapy modification by recognizing and categorizing the physical activity. Further, this desired adaptive therapy should be achiev...

The objective and automated detection of pneumonia represents a serious challenge in medical imaging, because the signs of the illness are not obvious in CT or X-ray scans. Further on, it is also an important task, since millions of people die of pneumonia every year. The main goal of this paper is to propose a solution for the above mentioned prob...

The ability of healthcare workers to learn proper hand hygiene has been an understudied area of research. Generally, hand hygiene skills are regarded as a key contributor to reduce critical infections and healthcare-associated infections. In a clinical setup, at a Neonatal Intensive Care Unit (NICU), the outcome of a multi-modal training initiative...

Real-time multilingual phrase detection from/during online video presentations—to support instant remote diagnostics—requires near real-time visual (textual) object detection and preprocessing for further analysis. Connecting remote specialists and sharing specific ideas is most effective using the native language. The main objective of this paper...

Automatic brain tumor segmentation from multimodal MRI plays a significant role in assisting the diagnosis, treatment, and surgery of glioblastoma and lower glade glioma. In this article, we propose applying several deep learning techniques implemented in AWS SageMaker Framework. The different CNN architectures are adapted and fine-tuned for our pu...

The accurate and reliable segmentation of gliomas from magnetic resonance image (MRI) data has an important role in diagnosis, intervention planning, and monitoring the tumor’s evolution during and after therapy. Segmentation has serious anatomical obstacles like the great variety of the tumor’s location, size, shape, and appearance and the modifie...

In the case of diabetes mellitus physical activity does have a high effect on the glycemic state of the patients. This is especially regarding the patients with Type 1 diabetes mellitus, who need external insulin administration in their daily life. Nevertheless, physical activity - as one source of stress - is underrepresented in the decisions of p...

Suppressed fuzzy c-means clustering was proposed as an attempt to combine the better properties of hard and fuzzy c-means clustering, namely the quicker convergence of the former and the finer partition quality of the latter. In the meantime, it became much more than that. Its competitive behavior was revealed, based on which it received two genera...

Card counting is a family of casino card game advantage gambling strategies, in which a player keeps a mental tally of the cards played in order to calculate whether the next hand is likely to be in the favor of the player or the dealer. A card counting system assigns point values (weights) to the cards. Summing the point values of the already play...

The development of brain tumor segmentation techniques based on multi-spectral MR image data has relevant impact on the clinical practice via better diagnosis, radiotherapy planning and follow-up studies. This task is also very challenging due to the great variety of tumor appearances, the presence of several noise effects, and the differences in s...

In this paper, we experiment with methods for obtaining binary sequences with a random probability mass function and with low autocorrelation and use it to generate ambiguous outcomes. Outputs from a neural network are mixed and shuffled, resulting in binary sequences whose probability mass function is non-convergent, constantly moving and changing...

Absolute values in magnetic resonance image data do not say anything about the investigated tissues. All these numerical values are relative, they depend on the imaging device and they may vary from session to session. Consequently, there is a need for histogram normalization before any other processing is performed on MRI data. The Brain Tumor Seg...

The number of medical imaging devices is quickly and steadily rising, generating an increasing amount of image records day by day. The number of qualified human experts able to handle this data cannot follow this trend, so there is a strong need to develop reliable automatic segmentation and decision support algorithms. The Brain Tumor Segmentation...

According to WHO estimates, 400 million people suffer from diabetes, and this number is likely to double by year 2030. Unfortunately, diabetes can have severe complications like glaucoma or retinopathy, which both can cause blindness. The main goal of our research is to provide an automated procedure that can detect retinopathy-related lesions of t...

In this paper a palm vein identification system is presented, which exploits the strength of convolutional neural network (CNN) architectures. We built and compared six different CNN approaches for biometric identification based on palm images. Four of them were developed by applying transfer learning and fine-tuning techniques to relevant deep lea...

Accuracy is the most important quality marker in medical image segmentation. However, when the task is to handle large volumes of data, the relevance of processing speed rises. In machine learning solutions the optimization of the feature set can significantly reduce the computational load. This paper presents a method for feature selection and app...

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions...

Finding nearest neighbors in high-dimensional spaces is a very expensive task. Locality-sensitive hashing is a general dimension reduction technique that maps similar elements closely in the hash space, streamlining near neighbor lookup. In this paper we propose a variable genome length biased random key genetic algorithm whose encoding facilitates...

Several hundreds of thousand humans are diagnosed with brain cancer every year, and the majority dies within the next two years. The chances of survival could be easiest improved by early diagnosis. This is why there is a strong need for reliable algorithms that can detect the presence of gliomas in their early stage. While an automatic tumor detec...

In this paper we present a biometric system based on dorsal hand vein recognition. The preprocessing steps are tuned for image similar or captured with the same scanner as used for the creation of NCUT database. Image quality was improved according to the segmentation method applied. A coarse segmentation technique based on ordinal image encoding h...

The development of automatic tumor detection and segmentation procedures enables the computers to preprocess huge sets of MRI records and draw the attention of medical staff upon suspected positive cases. This paper proposes a machine learning solution based on binary decision trees and random forest technique, trained to provide accurate segmentat...

The valve-sparing aortic root surgery is a common treatment of several aortic diseases. Although, the aortic valves are generally fixed in an even distribution during these operations, recently developed special tools enable the back-sewing of the valves in the original, patient specific distribution that likely provides better hemodynamic function...

Early repolarization pattern (ERP), a form of J-wave syndromes, was considered long time a benign ECG phenomenon. However, recent data confirmed that certain phenotypes of ERP are related to an increased risk of sudden cardiac death (idiopathic ventricular fibrillation). Our paper gives a short and practical update regarding the main issues related...

Introduction: Hand hygiene is probably the most effective tool of nosocomial infection prevention, however, proper feedback and control is needed to develop the individual hand hygiene practice. Aim: Assessing the efficiency of modern education tools, and digital demonstration and verification equipment during their wide-range deployment. Metho...

Ultraviolet spectrum markers are widely used for hand hygiene quality assessment, although their microbiological validation has not been established. A microbiology-based assessment of the procedure was conducted. Twenty-five artificial hand models underwent initial full contamination, then disinfection with UV-dyed hand rub solution, digital imagi...

Objective. The possible effect of blood pressure measurements per se on heart rate variability (HRV) was studied in the setting of concomitant ambulatory blood pressure monitoring (ABPM) and Holter ECG monitoring (HM). Methods. In 25 hypertensive patients (14 women and 11 men, mean age: 58.1 years), 24-hour combined ABPM and HM were performed. For...

Coronary artery disease represents one of the leading reasons of death worldwide, and acute coronary syndromes are their most devastating consequences. It is extremely important to identify the patients at risk for developing an acute myocardial infarction, and this goal can be achieved using noninvasive imaging techniques. Coronary computed tomogr...

One of the main challenges in the field of clustering is creating algorithms that are both accurate and robust. The fuzzy-possibilistic product partition c-means clustering algorithm was introduced with the main goal of producing accurate partitions in the presence of outlier data. This chapter presents several clustering algorithms based on the fu...

In this article we propose and tune a discriminative model based on Random Forest (RF) to accomplish brain tumor segmentation in multimodal MR images. The objective of tuning is to establish the optimal parameter values and the most significant constraints of the dis-criminative model. During the building of the RF classifier, the algorithm evaluat...

Detecting clusters of different sizes represents a serious difficulty for all c-means clustering models. This study investigates the set of various modified fuzzy c-means clustering algorithms within the bounds of the probabilistic constraint, from the point of view of their sensitivity to cluster sizes. Two numerical frameworks are constructed, on...

This comparative study employs several modified versions of the fuzzy c-means algorithm in image color reduction, with the aim of assessing their accuracy and efficiency. To assure equal chances for all algorithms, a common framework was established that preprocesses input images in terms of a preliminary color quantization, extraction of histogram...

Early detection is the key of success in the treatment of tumors. Establishing methods that can identify the presence and position of tumors in their early stage is a current great challenge in medical imaging. This study proposes a machine learning solution based on binary decision trees and random forest technique, aiming at the detection and acc...

Background: Graph-based hierarchical clustering algorithms become prohibitively costly in both execution time and storage space, as the number of nodes approaches the order of millions. Objective: A fast and highly memory efficient Markov clustering algorithm is proposed to perform the classification of huge sparse networks using an ordinary per...

The aim of this study was to objectively assess the hand hygiene performance of medical students. Hand rubbing technique was evaluated by employing innovative UV-light-based imaging technology, identifying patterns and trends in missed areas after applying WHO's six-step protocol. This specially designed hand hygiene education and assessment progra...

Objective . The aim of this study is to define the normal range for average real variability (ARV) and to establish whether it can be considered as an additional cardiovascular risk factor. Methods . In this observational study, 110 treated hypertensive patients were included and admitted for antihypertensive treatment adjustment. Circadian blood p...