다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

TELKOMNIKA : Telecommunication, Computing, Electronics and Control

Sickle cell anemia, which is also called sickle cell disease (SCD), is a hematological disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death. The key function of red blood cells (erythrocytes) is to supply all the parts of the human body with oxygen. Red blood cells (RBCs) form a crescent or sickle shape when sickle cell anemia affects them. This abnormal shape makes it difficult for sickle cells to move through the bloodstream, hence decreasing the oxygen flow. The precise classification of RBCs is the first step toward accurate diagnosis, which aids in evaluating the danger level of sickle cell anemia. The manual classification methods of erythrocytes require immense time, and it is possible that errors may be made throughout the classification stage. Traditional computer-aided techniques, which have been employed for erythrocyte classification, are based on handcrafted features techniques, and their performance relies on the selected features. They also are very sensitive to different sizes, colors, and complex shapes. However, microscopy images of erythrocytes are very complex in shape with different sizes. To this end, this research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content. These models are different in the number of layers and learnable filters. The available datasets of red blood cells with sickle cell disease are very small for training deep learning models. Therefore, addressing the lack of training data is the main aim of this paper. To tackle this issue and optimize the performance, the transfer learning technique is utilized. Transfer learning does not significantly affect performance on medical image tasks when the source domain is completely different from the target domain. In some cases, it can degrade the performance. Hence, we have applied the same domain transfer learning, unlike other methods that used the ImageNet dataset for transfer learning. To minimize the overfitting effect, we have utilized several data augmentation techniques. Our model obtained state-of-the-art performance and outperformed the latest methods by achieving an accuracy of 99.54% with our model and 99.98% with our model plus a multiclass SVM classifier on the erythrocytesIDB dataset and 98.87% on the collected dataset.

https://www.mdpi.com/2079-9292/9/3/427/pdf

Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis

Smart cities are a future reality for municipalities around the world. Healthcare services play a vital role in the transformation of traditional cities into smart cities. In this paper, we present a ubiquitous and quality computer-aided blood analysis service for the detection and counting of white blood cells (WBCs) in blood samples. WBCs also called leukocytes or leucocytes are the cells of the immune system that are involved in protecting the body against both infectious disease and foreign invaders. Analysis of leukocytes provides valuable information to medical specialists, helping them in diagnosing different important hematic diseases, such as AIDS and blood cancer (Leukaemia). However, this task is prone to errors and can be time-consuming. A mobile-cloud-assisted detection and classification of leukocytes from blood smear images can enhance accuracy and speed up the detection of WBCs. In this paper, we propose a smartphone-based cloud-assisted resource aware framework for localization of WBCs within microscopic blood smear images using a trained multi-class ensemble classification mechanism in the cloud. In the proposed framework, nucleus is first segmented, followed by extraction of texture, statistical, and wavelet features. Finally, the detected WBCs are categorized into five classes: basophil, eosinophil, neutrophil, lymphocyte, and monocyte. Experimental results on numerous benchmark databases validate the effectiveness and efficiency of the proposed system in comparison to the other state-of-the-art schemes.

Leukocytes Classification and Segmentation in Microscopic Blood Smear: A Resource-Aware Healthcare Service in Smart Cities

Current trends in nanomaterial embedded field effect transistor-based biosensor

In medical field, the number of red blood cells (RBC) are used as an indicator to detect the type of diseases such as malaria, anemia, leukemia and etc. The problems using manual counting of RBC under the microscope is tend to give inaccurate result and errors. This paper proposed a method to count a total number of RBC in peripheral blood smear image by using circular Hough transform (CHT) method. The process involves preprocessing and segmentation a single cell image of RBC after cropping it to get the minimum and maximum radius of cell. Then, CHT method is applied to detect and count the number of RBC based on the range radius of cells. The results show that from ten samples of peripheral blood smear image, the accuracy using CHT method is 91.87%.

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Automated Red Blood Cells Counting in Peripheral Blood Smear Image Using Circular Hough Transform

Body temperature of the baby is an important element that will inform the actual health condition of a baby. Parents and caregiver occasionally do not aware of drastic increase in body temperature in a short period of time and febrile seizure may happen that could leads to epilepsy. In Malaysia, device that could monitor the infant body's temperature is already available. However, the device could not continuously be used for a long hours and create discomfort to the babies due to its size of devices. Therefore, a small, lightweight device that continuously monitors the body temperature and comfortably used by baby is developed. It directly helps parents by alerting them whenever the baby's body temperature increased higher than normal a degree. This system monitors the vital parameter which is the body temperature by using a wearable sensor. The information then transferred to their parents through a wireless network. The system is extended for interfacing with the mobile phones to enable remote monitoring. Architecture of the system consists of a wearable sensor for monitoring the vital parameter and a sound buzzer where all of the component be controlled by a single microcontroller, the ESPresso Lite V2.0 based on ESP8266 and supplied by the lithium ion polymer battery. Even though the system is more focusing on temperature monitoring only, it can be further expanded or upgrade to monitor other vital parameter such as heart rate, oxygen saturation, respiration rate or any other parameter.

IoT (Internet of Things) Based Infant Body Temperature Monitoring

Sickle cell anemia (SCA) is a serious hematological disorder, where affected patients are frequently hospitalized throughout a lifetime and even can cause death. The manual method of detecting and classifying abnormal cells of SCA patient blood film through a microscope is time-consuming, tedious, prone to error, and require a trained hematologist. The affected patient has many cell shapes that show important biomechanical characteristics. Hence, having an effective way of classifying the abnormalities present in the SCA disease will give a better insight into managing the concerned patient's life. This work proposed algorithm in two-phase firstly, automation of red blood cells (RBCs) extraction to identify the RBC region of interest (ROI) from the patient’s blood smear image. Secondly, deep learning AlexNet model is employed to classify and predict the abnormalities presence in SCA patients. The study was performed with (over 9,000 single RBC images) taken from 130 SCA patient each class having 750 cells. To develop a shape factor quantification and general multiscale shape analysis. We reveal that the proposed framework can classify 15 types of RBC shapes including normal in an automated manner with a deep AlexNet transfer learning model. The cell's name classification prediction accuracy, sensitivity, specificity, and precision of 95.92%, 77%, 98.82%, and 90% were achieved, respectively.

http://ijai.iaescore.com/index.php/IJAI/article/download/20420/13041

A deep learning AlexNet model for classification of red blood cells in sickle cell anemia

Image processing comes with various techniques. It uses a series of framework to transform an input image into an output image. In recent times, image processing technique has been extensively used in medical area. In order to overcome the problems of manual diagnosis in identifying the morphology of blood cells, the automated diagnosis is often used. Manual diagnosis required the observation of blood sample by expert hematologist and pathologist. This method may suffer from the presence of non-standard precision of human visual inspection. Due to this problem, this paper focused on semi-automated diagnosis that used image processing technique to perform the segmentation of the nucleus in white blood cell (WBC). Several image processing techniques are used including the active contour method. The results obtained are based on the parameter values obtained from segmentation process. The parameter value is calculated from the roundness equation. The value of 0.80 can be used to describe as a single leukocyte.

Segmentation of White Blood Cell Nucleus Using Active Contour

Whilst laboratory-on-chip cell separation systems using dielectrophoresis are increasingly reported in the literature, many systems are afflicted by factors which impede “real world” performance, chief among these being cell loss (in dead spaces, attached to glass and tubing surfaces, or sedimentation from flow), and designs with large channel height-to-width ratios (large channel widths, small channel heights) that make the systems difficult to interface with other microfluidic systems. In this paper, we present a scalable structure based on 3D wells with approximately unity height-to-width ratios (based on tubes with electrodes on the sides), which is capable of enriching yeast cell populations whilst ensuring that up to 94.3% of cells processed through the device can be collected in tubes beyond the output.

Mohd Azhar Abdul Razak

Papers

Automated Red Blood Cells Counting in Peripheral Blood Smear Image Using Circular Hough Transform

IoT (Internet of Things) Based Infant Body Temperature Monitoring

A deep learning AlexNet model for classification of red blood cells in sickle cell anemia

Segmentation of White Blood Cell Nucleus Using Active Contour

Efficient dielectrophoretic cell enrichment using a dielectrophoresis-well based system.