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Search results for: DISEASE RECOGNITION
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The Influence of Selecting Regions from Endoscopic Video Frames on The Efficiency of Large Bowel Disease Recognition Algorithms
PublicationThe article presents our research in the field of the automatic diagnosis of large intestine diseases on endoscopic video. It focuses on the methods of selecting regions of interest from endoscopic video frames for further analysis by specialized disease recognition algorithms. Four methods of selecting regions of interest have been discussed: a. trivial, b. with the deletion of characteristic, endoscope specific additions to the...
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Dependable Integration of Medical Image Recognition Components
PublicationComputer driven medical image recognition may support medical doctors in the diagnosis process, but requires high dependability considering potential consequences of incorrect results. The paper presentsa system that improves dependability of medical image recognition by integration of results from redundant components. The components implement alternative recognition algorithms of diseases in thefield of gastrointestinal endoscopy....
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Endoscopic Videos Deinterlacing and On-Screen Text and Light Flashes Removal and Its Influence on Image Analysis Algorithms' Efficiency
PublicationIn this article, deinterlacing and removing on- screen text and light flashes methods on endoscopic video images are discussed. The research is intended to improve disease recognition algorithms' performance. In the article, four configurations of deinterlacing methods and another four configurations of text and flashes removal methods are described and examined. The efficiency of endoscopic video analysis algorithms is measured...
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Artificial intelligence support for disease detection in wireless capsule endoscopy images of human large bowel
PublicationIn the work the chosen algorithms of disease recognition in endoscopy images were described and compared for theirs efficiency. The algorithms were estimated with regard to utility for application in computer system's support for digestive system's diagnostics. Estimations were achieved in an advanced testing environment, which was built with use of the large collection of endoscopy movies received from Medical University in Gdańsk....
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Knowledge representation of motor activity of patients with Parkinson’s disease
PublicationAn approach to the knowledge representation extraction from biomedical signals analysis concerning motor activity of Parkinson disease patients is proposed in this paper. This is done utilizing accelerometers attached to their body as well as exploiting video image of their hand movements. Experiments are carried out employing artificial neural networks and support vector machine to the recognition of characteristic motor activity...
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AN ALGORITHM FOR PORTAL HYPERTENSIVE GASTROPATHY RECOGNITION ON THE ENDOSCOPIC RECORDINGS
PublicationSymptoms recognition of portal hypertensive gastropathy (PHG) can be done by analysing endoscopic recordings, but manual analysis done by physician may take a long time. This increases probability of missing some symptoms and automated methods may be applied to prevent that. In this paper a novel hybrid algorithm for recognition of early stage of portal hypertensive gastropathy is proposed. First image preprocessing is described....
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Trustworthy Applications of ML Algorithms in Medicine - Discussion and Preliminary Results for a Problem of Small Vessels Disease Diagnosis.
PublicationML algorithms are very effective tools for medical data analyzing, especially at image recognition. Although they cannot be considered as a stand-alone diagnostic tool, because it is a black-box, it can certainly be a medical support that minimize negative effect of human-factors. In high-risk domains, not only the correct diagnosis is important, but also the reasoning behind it. Therefore, it is important to focus on trustworthiness...
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Molecularly imprinted polymers for the detection of volatile biomarkers
PublicationIn the field of cancer detection, the development of affordable, quick, and user-friendly sensors capable of detecting various cancer biomarkers, including those for lung cancer (LC), holds utmost significance. Sensors are expected to play a crucial role in the early-stage diagnosis of various diseases. Among the range of options, sensors emerge as particularly appealing for the diagnosis of various diseases, owing to their cost-effectiveness,...
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Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy
PublicationThe diabetic retinopathy is a disease caused by long-standing diabetes. Lack of effective treatment can lead to vision impairment and even irreversible blindness. The disease can be diagnosed by examining digital color fundus photographs of retina. In this paper we propose deep learning approach to automated diabetic retinopathy screening. Deep convolutional neural networks (CNN) - the most popular kind of deep learning algorithms...
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Using Convolutional Neural Networks for Corneal Arcus Detection Towards Familial Hypercholesterolemia Screening
PublicationFamilial hypercholesterolemia (FH) is a highly undiagnosed disease. Among FH patients, the onset of premature coronary artery disease is 13 times higher than in the general population. Early diagnosis and treatment is essential to prevent cardiovascular diseases and their complications, and to prolong life. One of the clinical criteria of FH is the occurrence of a corneal arcus (CA) among patients, especially those under 45 years...
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Toxoplasma gondii recombinant antigens as tools for serodiagnosis of human toxoplasmosis: current status of studies
PublicationToxoplasma gondii is a parasitic protozoan which is the cause of toxoplasmosis. Although human toxoplasmosis in healthy adults is usually asymptomatic, serious disease can occur in the case of congenital infections and immunocompromised individuals. Furthermore, despite the exact recognition of its etiology, it still presents a diagnostic problem. Diagnosis of toxoplasmosis is mainly based on the results of serological tests detecting...
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Real-Time Gastrointestinal Tract Video Analysis on a Cluster Supercomputer
PublicationThe article presents a novel approach to medical video data analysis and recognition. Emphasis has been put on adapting existing algorithms detecting le- sions and bleedings for real time usage in a medical doctor's office during an en- doscopic examination. A system for diagnosis recommendation and disease detec- tion has been designed taking into account the limited mobility of the endoscope and the doctor's requirements. The...
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Role of cholesterol in substrate recognition by -secretase
Publication-Secretase is an enzyme known to cleave multiple substrates within their transmembrane domains, with the amyloid precursor protein of Alzheimer’s Disease among the most prominent examples. The activity of -secretase strictly depends on the membrane cholesterol content, yet the mechanistic role of cholesterol in the substrate binding and cleavage remains unclear. In this work, we used all-atom molecular dynamics simulations to examine...
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Procognitive activity of nitric oxide inhibitors and donors in animal models
PublicationNitric oxide is a small gaseous molecule that plays important roles in the majority of biological functions. Impairments of NO-related pathways contribute to the majority of neurological disorders, such as Alzheimer’s disease (AD), and mental disorders, such as schizophrenia. Cognitive decline is one of the most serious impairments accompanying both AD and schizophrenia. In the present study, the activities of NO donors, slow (spermine...
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Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier
PublicationIn recent years, a lot of attention has been paid to using radiology imaging to automatically find COVID-19. (1) Background: There are now a number of computer-aided diagnostic schemes that help radiologists and doctors perform diagnostic COVID-19 tests quickly, accurately, and consistently. (2) Methods: Using chest X-ray images, this study proposed a cutting-edge scheme for the automatic recognition of COVID-19 and pneumonia....