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Manju, K.
- Blood Leakage Monitoring System for the Applications in Haemodialysis Therapy by using IEEE 802.15.4 Standard
Authors
Source
Biometrics and Bioinformatics, Vol 8, No 7 (2016), Pagination: 194-196Abstract
The purpose of this paper is to design, fabricate, and characterize of a bracelet monitoring device for blood leakage detection during the haemodialysis treatment. The design includes a photo interrupter, a Bluetooth 4 wireless module, power, and alert components. The validation results show that it only needs a very small amount of blood (0.01 ml), and takes 1.6 s to detect a blood leakage. Furthermore, the lifetime of the battery on this device is longer than the currently available commercial products. It can continuously give out an alert for 18-h long and continuously monitor up to 41 h. In addition, the transmission range of Bluetooth wireless signal can be extended to 23 m. As long as the patients wear this bracelet blood leakage detector during the haemodialysis therapy and affix the absorbent material onto the junction of fistula, any blood leakage can be detected. As the absorbent material is placed at the light sensing position of the photo interrupter, which causes the received light intensity to change during blood leakage. Once a blood leakage occurs, the absorbent material absorbs the blood due to the capillary action and triggers the alarm system. A warning light will also be activated, and a leakage occurrence is transmitted to the healthcare stations alarming healthcare workers via the Bluetooth wireless. The healthcare workers can take appropriate action immediately to prevent any risks to the patients during haemodialysis therapy. The proposed blood leakage monitoring system can improve the current medical approach for the haemodialysis therapy.
Keywords
Haemodialysis, Blood Leakage Detection, Photo Interrupter, Bluetooth 4.0.
- Contourlet Transform and PNN Based Brain Tumor Classification
Authors
Source
International Journal of Innovative Research and Development, Vol 3, No 4 (2014), Pagination:Abstract
In this paper a new method is proposed for Brain Tumor classification using a Probabilistic neural network with contourlet transformation. The conventional methods like Computerized Tomography (CT) and Magnetic Resonance Images (MRI) are based on human inspection for tumor classification and detection. These methods are inefficient and are also non reproducible for large amount of data. Also, those methods include noise due to operator performance which creates serious inaccuracies in tumor classification. Neural Networks shows good results in medical diagnosis which is combined here with contourlet transform. Decision making is based on two steps (i) Image reduction and Feature extraction using contourlet transform (ii) Classification using probabilistic Neural Network(PNN). Performance evaluation on various brain tumor images shows fast and better recognition rate and also low computational requirements, when compared to previous classifiers.
Keywords
Brain tumor image classification, Probabilistic Neural Networks, Contourlet Transform, Dimensionality Reduction, Feature ExtractionFull Text
- Fundus Image Classification using Hybridized GLCM Features and Wavelet Features
Authors
1 Department of Electronics and Communication Engineering, Sona College of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 3 (2021), Pagination: 2372-2375Abstract
We find the usefulness of computers in every field including medical field. Scanning the affected part has become a standard study. Diagnosing a disease at the right time, i.e. early detection, from the study of images enables the physician to take right decision and provide proper treatment to the patient. With the alarming growth of population, it is difficult for every individual patient to get a second opinion from medical expert. In these situations, computer-aided automatic diagnosis system will be much helpful. Diabetic retinopathy is a disorder that arises from increase in blood glucose level. Based on the severity, it has been distinguished into four stages. Diagnosing diabetic retinopathy at an early stage from retinal images and providing proper treatment will save the patient from severe vision loss. The proposed method adopts hybridized GLCM features and wavelet features to classify the fundus images according to the severity of the disease. The method is tested with fundus images collected from Indian Diabetic Retinopathy Dataset.Keywords
Fundus Image, GLCM, WDM Features, Diabetic Retinopathy, Classification.References
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