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Search results for: AUTOMATED DIAGNOSIS
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Fast Real-Time RDFT- and GDFT-Based Direct Fault Diagnosis of Induction Motor Drive
PublicationThis paper presents the theoretical analysis and experimental verification of a direct fault harmonic identification approach in a converter-fed electric drive for automated diagnosis purposes. On the basis of the analytical model of the proposed real-time direct fault diagnosis, the fault-related harmonic component is calculated using recursive DFT (RDFT) and Goertzel DFT (GDFT), applied instead of the full spectrum calculations...
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Deep Learning-Based Cellular Nuclei Segmentation Using Transformer Model
PublicationAccurate segmentation of cellular nuclei is imperative for various biological and medical applications, such as cancer diagnosis and drug discovery. Histopathology, a discipline employing microscopic examination of bodily tissues, serves as a cornerstone for cancer diagnosis. Nonetheless, the conventional histopathological diagnosis process is frequently marred by time constraints and potential inaccuracies. Consequently, there...
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DIAGNOSIS OF MALIGNANT MELANOMA BY NEURAL NETWORK ENSEMBLE-BASED SYSTEM UTILISING HAND-CRAFTED SKIN LESION FEATURES
PublicationMalignant melanomas are the most deadly type of skin cancer but detected early have high chances for successful treatment. In the last twenty years, the interest of automated melanoma recognition detection and classification dynamically increased partially because of public datasets appearing with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task...
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Novel method of lung area extraction in chest perfusion computed tomography
PublicationChest perfusion computed tomography (pCT) is a useful technique in the medical diagnosis of how organs function. Perfusion CT scans are used to calculate perfusion parameters. In the case of automated methods of lung perfusion parameters calculation, the prior extraction of the lung area is desired to avoid unnecessary calculation in an area outside the lung cross-section and to avoid wasting time on processing signals of no diagnostic...
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LCT, PIV and IR Imaging Detection in Selected Technical and Biomedical Applications
PublicationThermochromic liquid crystals (TLC), Particle Image Velocimetry (PIV) and Infrared Imaging Themography (IR) have been successfully used in non-intrusive technical, industrial and biomedical studies and applications. These four tools (based on the desktop computers) have come together during the past two decades to produce a powerful advanced experimental technique as a judgment of quality of information that cannot be obtained...
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Liquid Crystals, PIV and IR-Photography in Selected Technical and Biomedical Applications
PublicationThermochromic liquid crystals (TLC), Particle Image Velocimetry (PIV), Infrared Imaging Themography (IR) and True-Colour Digital Image Processing (TDIP) have been successfully used in non-intrusive technical, industrial and biomedical studies and applications. These four tools (based on the desktop computers) have come together during the past two decades to produce a powerful advanced...
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Analogue CMOS ASICs in Image Processing Systems
PublicationIn this paper a survey of analog application specific integrated circuits (ASICs) for low-level image processing, called vision chips, is presented. Due to the specific requirements, the vision chips are designed using different architectures best suited to their functions. The main types of the vision chip architectures and their properties are presented and characterized on selected examples of prototype integrated circuits (ICs)...
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Interpretable deep learning approach for classification of breast cancer - a comparative analysis of multiple instance learning models
PublicationBreast cancer is the most frequent female cancer. Its early diagnosis increases the chances of a complete cure for the patient. Suitably designed deep learning algorithms can be an excellent tool for quick screening analysis and support radiologists and oncologists in diagnosing breast cancer.The design of a deep learning-based system for automated breast cancer diagnosis is not easy due to the lack of annotated data, especially...
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A Development of a Capacitive Voltage Divider for High Voltage Measurement as Part of a Combined Current and Voltage Sensor
PublicationThis article deals with the development of capacitive voltage divider for high voltage measurements and presents a method of analysis and optimization of its parameters. This divider is a part of a combined voltage and current sensor for measurements in high voltage power networks. The sensor allows continuous monitoring of the network distribution status and performs a quick diagnosis and location of possible network failures....
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Smart Approach for Glioma Segmentation in Magnetic Resonance Imaging using Modified Convolutional Network Architecture (U-NET)
PublicationSegmentation of a brain tumor from magnetic resonance multimodal images is a challenging task in the field of medical imaging. The vast diversity in potential target regions, appearance and multifarious intensity threshold levels of various tumor types are few of the major factors that affect segmentation results. An accurate diagnosis and its treatment demand strict delineation of the tumor affected tissues. Herein, we focus on...
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Detection of Alzheimer's disease using Otsu thresholding with tunicate swarm algorithm and deep belief network
PublicationIntroduction: Alzheimer’s Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for many researchers. The structural Magnetic Resonance Imaging (sMRI) is an extensively used imaging technique in detection of AD, because...
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Rediscovering Automatic Detection of Stuttering and Its Subclasses through Machine Learning—The Impact of Changing Deep Model Architecture and Amount of Data in the Training Set
PublicationThis work deals with automatically detecting stuttering and its subclasses. An effective classification of stuttering along with its subclasses could find wide application in determining the severity of stuttering by speech therapists, preliminary patient diagnosis, and enabling communication with the previously mentioned voice assistants. The first part of this work provides an overview of examples of classical and deep learning...
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The design of an intelligent medical space supporting automated patient interviewing
PublicationThe article presents the architecture and results of implementing an application for the intelligent medical space UbiDoDo (Ubiquitous Domestic Doctor's Office). The main purpose of the application is real-time monitoring of the biomedical parameters of a patient in his domestic environment. It allows an immediate reaction to appearing symptoms and provides means to automatically interview the patient and deliver his results to...
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Attention-Based Deep Learning System for Classification of Breast Lesions—Multimodal, Weakly Supervised Approach
PublicationBreast cancer is the most frequent female cancer, with a considerable disease burden and high mortality. Early diagnosis with screening mammography might be facilitated by automated systems supported by deep learning artificial intelligence. We propose a model based on a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) classifier able to train under data scarcity effectively. We used a private...
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Melanoma skin cancer detection using mask-RCNN with modified GRU model
PublicationIntroduction: Melanoma Skin Cancer (MSC) is a type of cancer in the human body; therefore, early disease diagnosis is essential for reducing the mortality rate. However, dermoscopic image analysis poses challenges due to factors such as color illumination, light reflections, and the varying sizes and shapes of lesions. To overcome these challenges, an automated framework is proposed in this manuscript. Methods: Initially, dermoscopic...
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Automated hearing loss type classification based on pure tone audiometry data
PublicationHearing problems are commonly diagnosed with the use of tonal audiometry, which measures a patient’s hearing threshold in both air and bone conduction at various frequencies. Results of audiometry tests, usually represented graphically in the form of an audiogram, need to be interpreted by a professional audiologist in order to determine the exact type of hearing loss and administer proper treatment. However, the small number of...
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DEEP LEARNING BASED ON X-RAY IMAGING IMPROVES COXARTHROSIS DETECTION
PublicationObjective: The purpose of the study was to create an Artificial Neural Network (ANN) based on X-ray images of the pelvis, as an additional tool to automate and improve the diagnosis of coxarthrosis. The research is focused on joint space narrowing, which is a radiological symptom showing the thinning of the articular cartilage layer, which is translucent to X-rays. It is the first and the most important of the radiological signs...
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Precise Identification of Different Cervical Intraepithelial Neoplasia (CIN) Stages, Using Biomedical Engineering Combined with Data Mining and Machine Learning
PublicationCervical cancer (CC) is one of the most common female cancers worldwide. It remains a significant global health challenge, particularly affecting women in diverse regions. The pivotal role of human papillomavirus (HPV) infection in cervical carcinogenesis underscores the critical importance of diagnostic strategies targeting both HPV infection and cervical...
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Segmentation-Based BI-RADS ensemble classification of breast tumours in ultrasound images
PublicationBackground: The development of computer-aided diagnosis systems in breast cancer imaging is exponential. Since 2016, 81 papers have described the automated segmentation of breast lesions in ultrasound images using arti- ficial intelligence. However, only two papers have dealt with complex BI-RADS classifications. Purpose: This study addresses the automatic classification of breast lesions into binary classes (benign vs. ma- lignant)...
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SEMG signal database for the automated upper limb rehabilitation process
Open Research DataAn automated rehabilitation device control system requires information about the patient's physiological condition. This is possible thanks to the use of biological feedback in the form of electromyography and surface signals (Surface Electromyography, SEMG).
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SkinDepth - synthetic 3D skin lesion database
Open Research DataSkinDepth is the first synthetic 3D skin lesion database. The release of SkinDepth dataset intends to contribute to the development of algorithms for:
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D-loop sequences retrieved from Canis lupus familiaris mitochondrial genome
Open Research DataCanine mitochondrial genome is built of 16727 bp. Non-coding control region (mtCR), called also D-loop, begins with 15458 nucleotide and ends with 16727 nucleotide. The length of this fragment is 1270 bp (Kim et al., 1998). D-loop region is responsible for replication and transcription of mitochondrial DNA. Mutations that occur within it may cause irregularity...