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Electrocardiography (ECG) signals and the information obtained through the analysis of these signals constitute the main source of diagnosis of many cardiovascular system diseases. Pulmonary arterial hypertension (PAH) is a disease without any known treatment and causes failure in the right side of the heart and finally death. In this study, experimentally measured ECG signals were analyzed in frequency...
Wireless capsule endoscopy is an important advanced diagnostics method. It produces huge amount of images during travel through patient's digestive tract and that usually requires automated analysis. One of the most important abnormalities is bleeding and automated segmentation for bleeding detection is an active research topic. In this paper we propose an algorithm for automated segmentation for...
In this paper, we propose a system that is capable of automatically differentiating between normal and abnormal heartbeats of patients using signals acquired from electrocardiography (ECG). The components of the ECG signals, that are PQRST intervals, were studied to acquire features for classification. Different time intervals of p-wave, QRS complex and t-wave were used as features. These features...
Thyroid gland influences the metabolic processes of human body due to the fact that it produces hormones. Hyperthyroidism in caused due to increase in the production of thyroid hormones. In this paper a methodology using an online ensemble of decision trees to detect thyroid-related diseases is proposed. The aim of this work is to improve the diagnostic accuracy of thyroid disease. Initially, feature...
Obstructive Sleep Apnea is a disease that occurs due to respiratory arrest in sleep. The diagnosis of the disease is made with the polysomnography device, which is the gold standard method of diagnosis. Diagnosis is performed by sleep staging and respiratory scoring steps. Respiratory scoring is performed with at least four signals. Technical knowledge is required to connect the electrodes. Moreover,...
Multiple sclerosis (MS) interrupts communication between the brain and other parts of the body causing functional deterioration. Gait impairment is a common finding in MS, one caused by several neurological symptoms. We perform an event-specific analysis to study the variable impact of MS on gait components. Our results show that the mid-swing to heel strike (HS) phase of a gait cycle is the most...
Paddy is the most important crop in Asian country. Most of the people depend on rice for their food, so rice is considered as staple food in Asian country. Rice plant is affected by many diseases that affect the farmers in yield loss. In this paper, proposed a method for identification of Blast and Brown Spot diseases. Global threshold method has been applied and kNN classifier has been used to classify...
The infection in shrimp is a significant issue which makes decline in production. In this manner, the investigation of premium is the region on shrimp infection happens. By utilizing proficient computer programming software to consequently distinguish the shrimp infection by separating features. Image processing systems are utilized for location and acknowledgment of illnesses in different zones which...
In the world today there are number of skin diseases which are found in humans, animals and plants. The illness caused by bacteria or infections will be known as skin disease like yeast infection, allergy, eczema, brown spot. These skin diseases have dangerous effect on skin and they keep on spreading over time. To control them from spreading it is necessary to identify these diseases at their starting...
The cardiovascular health informatics system is developed using Laboratory Virtual Instrument Engineering Workbench (LabVIEW). The system work starts with the acquisition of ECG from the patient or ECG simulator, using ECG amplifier circuit. The signal from amplifier circuit is acquired in LabVIEW environment by using DAQ cards. The advantage of LabVIEW is that it has special toolkits, pallets and...
Automated and accurate classification of MR brain imagesis extremely important for medical analysis and interpretation. Overthe last decade numerous methods have already been proposed. In this paper, we presented a novel method to classify a givenMR brain image as normal or abnormal. The proposed method first employed wavelet transform to extract features from images, followed by applying principle...
Cotton is one of the most important cash crops in India. Every year the production of cotton is reducing due to the attack of the disease. Plant diseases are generally caused by pest insect and pathogens and decrease the productivity to large-scale if not controlled within time. This paper presents a system for detection and controlling of diseases on cotton leaf along with soil quality monitoring...
Citrus is nutrition fruit and required for human being. This is one for major cash crop in India. The various bacterial and microorganisms attack on plant affects various parts like stem, leaves and fruit. This research explain about implementation of image processing to determiner abnormalities and diseases over citrus leaves. This research model divided into four parts. First stage is image pre-processing...
Modern phenotyping and plant disease detection provide promising step towards food security and sustainable agriculture. In particular, imaging and computer vision based phenotyping offers the ability to study quantitative plant physiology. On the contrary, manual interpretation requires tremendous amount of work, expertise in plant diseases, and also requires excessive processing time. In this work,...
We consider the problem of domain shift in analyses of brain MRI data. While many different datasets are publicly available, most algorithms are still trained on a single dataset and often suffer the problem of limited and unbalanced sample sizes. In this work, we propose a surprisingly simple strategy to reduce the impact of domain shift - caused by different data sources and processing pipelines...
Similarity in appearance between various skin diseases, often makes it challenging for clinicians to identify the type of skin condition, and the accuracy is highly reliant on the level of expertise. There is also a great degree of subjectivity and inter/intra observer variability found in the clinical practices. In this paper, we propose a method for automatic skin diseases recognition that combines...
As medical imaging datasets grow, we are approaching the era of big data for radiologic decision support systems. This requires renewed efforts in dataset curation and labeling. We propose a methodology for weak labeling of medical images for attributes such as anatomy and disease that relies on image to sentence transformation. The methodology consists of three models, a convolutional neural network...
One of the main challenges of histological image analysis is the high dimensionality of the images. This can be addressed via summarizing techniques or feature engineering. However, such approaches can limit the performance of subsequent machine learning models, particularly when dealing with highly heterogeneous tissue samples. One possible alternative is to employ unsupervised learning to determine...
The exponential growth of high dimensional biological data has led to a rapid increase in demand for automated approaches for knowledge production. Existing methods rely on two general approaches to address this challenge: 1) the Theorydriven approach, which utilizes prior accumulated knowledge, and 2)the Data-driven approach, which solely utilizes the data to deduce scientific knowledge. Both of...
Accurate identification and understanding informative feature is important for early Alzheimer’s disease (AD) prognosis and diagnosis. In this paper, we propose a novel discriminative sparse learning method with relational regularization to jointly predict the clinical score and classify AD disease stages using multimodal features. Specifically, we apply a discriminative learning technique to expand...
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