Automated Detection of Human RBC in Diagnosing Sickle Cell Anemia with Laplacian of Gaussian Filter

Mohamad, Ahmad Sabry; Halim, Nur Syahirah Abdul; Nordin, Muhammad Noor; Hamzah, Roszymah; Sathar, Jameela

doi:10.1109/spc.2018.8704128

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…Several methods have been proposed to detect the sickleshaped RBC in SCD patients by [21,22,29,55,56,88,114,115,120,131,159,160] using methods such as random walk, Sobel edge, geometric features, Fuzzy C means clustering, LOG, WT, HT and morphological filters. The average accuracy reported was in the range of 85-95%.…”

Section: Morphology Based Anemia Classification Methodsmentioning

confidence: 99%

Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review

Navya

Prasad

Singh

2022

Med Biol Eng Comput

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Anemia is a blood disorder which is caused due to inadequate red blood cells and hemoglobin concentration. It occurs in all phases of life cycle but is more dominant in pregnant women and infants. According to the survey conducted by the World Health Organization (WHO) (McLean et al., Public Health Nutr 12(4):444–454, 2009), anemia affects 1.62 billion people constituting 24.8% of the population and is considered the world’s second leading cause of illness. The Peripheral Blood Smear (PBS) examination plays an important role in evaluating hematological disorders. Anemia is diagnosed using PBS. Being the most powerful analytical tool, manual analysis approach is still in use even though it is tedious, prone to errors, time-consuming and requires qualified laboratorians. It is evident that there is a need for an inexpensive, automatic and robust technique to detect RBC disorders from PBS. Automation of PBS analysis is very active field of research that motivated many research groups to develop methods using image processing. In this paper, we present a review of the methods used to analyze the characteristics of RBC from PBS images using image processing techniques. We have categorized these methods into three groups based on approaches such as RBC segmentation, RBC classification and detection of anemia, and classification of anemia. The outcome of this review has been presented as a list of observations. Graphical abstract

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Section: Morphology Based Anemia Classification Methodsmentioning

confidence: 99%

Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review

Navya

Prasad

Singh

2022

Med Biol Eng Comput

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“…Laplacian-based methods are sensitive to noises and when these methods are used on the images, the images have many unwanted edge points and noises. To handle this issue, the image is smoothed using Gaussian low-pass fltering in the LoG method [33,34]. In this study, the LoG flter was applied to smooth the images, sharpen the edge contours of the kidney stones, and reduce the noise on DUSX images.…”

Section: Laplacian Of Gaussian Filtering (Log)mentioning

confidence: 99%

Exploring the Effect of Image Enhancement Techniques with Deep Neural Networks on Direct Urinary System X‐Ray (DUSX) Images for Automated Kidney Stone Detection

Kilic,

Karabey Aksakalli,

Tumuklu Ozyer

et al. 2023

International Journal of Intelligent Systems

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In diagnosing kidney stone disease, clinical specialists often apply medical imaging techniques such as CT and US. Among these imaging techniques, is frequently chosen as the primary examination method in emergency services due to its low cost, accessibility, and low radiation levels. However, interpreting the images by inexperienced specialists can be challenging due to the low image quality and the presence of noise. In this study, we propose a computer-aided diagnosis system based on deep neural networks to assist clinical specialists in detecting kidney stones using Direct Urinary System (DUSX) images. Firstly, in consultation with clinical specialists, we created a new dataset composed of 630 DUSX images and presented it publicly. We also defined preprocessing steps that incorporate image enhancement techniques such as GF, LoG, BF, HE, CLAHE, and CBC to enable deep neural networks to perceive the images more clearly. With these techniques, we considered the noise reduction in the DUSX images and enhanced the poor quality, especially in terms of contrast. For each preprocessing step, we created models to detect kidney stones using YOLOv4 and Mask R-CNN architectures, which are common CNN-based object detectors. We examined the effects of the preprocessing steps on these models. To the best of our knowledge, the combination of BF and CLAHE which is called CBC in this study, has not been applied before in the literature to enhance DUSX images. In addition, this study is the first in its field in which the YOLOv4 and Mask R-CNN architectures have been used for the detection of kidney stones. The experimental results demonstrated the most accurate method is the YOLOv4 model, which includes the CBC preprocessing step, as the result model. This model shows that the accuracy rate, precision, recall, and F1-score were found as 96.1%, 99.3% 96.5%, and 97.9% respectively in the test set. According to these performance metrics, we expect that the proposed model will help to reduce the unnecessary radiation exposure and associated medical costs that come with CT scans.

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Classification of Sickle Cell Anemia Using Energy-Based KNN Classifier

Vadivel

Baskar

Sivakumar

et al. 2021

Lecture Notes in Networks and Systems

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Automated Detection of Human RBC in Diagnosing Sickle Cell Anemia with Laplacian of Gaussian Filter

Cited by 9 publications

References 11 publications

Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review

Analysis of red blood cells from peripheral blood smear images for anemia detection: a methodological review

Exploring the Effect of Image Enhancement Techniques with Deep Neural Networks on Direct Urinary System X‐Ray (DUSX) Images for Automated Kidney Stone Detection

Classification of Sickle Cell Anemia Using Energy-Based KNN Classifier

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