Improvement of complete malaria cell image segmentation

Wattana, Monlica; Boonsri, Tipwalee

doi:10.1109/icdim.2017.8244655

Cited by 7 publications

(2 citation statements)

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“…Other image processing methods, such as feature scaling, may also be employed to facilitate better feature extraction and can significantly increase the learning ability of the models used [5,32,33]. Furthermore, models can be developed to ascertain the concentration of the parasite in the blood smear to better understand the rapid progression of the disease and make an appropriate diagnosis [33].…”

Section: Parasite Classificationmentioning

confidence: 99%

Classification of Malaria Using Object Detection Models

et al. 2022

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Malaria poses a global health problem every day, as it affects millions of lives all over the world. A traditional diagnosis requires the manual inspection of blood smears from the patient under a microscope to check for the malaria parasite. This is often time consuming and subject to error. Thus, the automated detection and classification of the malaria type and stage of progression can provide a quicker and more accurate diagnosis for patients. In this research, we used two object detection models, YOLOv5 and scaled YOLOv4, to classify the stage of progression and type of malaria parasite. We also used two different datasets for the classification of stage and parasite type while assessing the viability of the dataset for the task. The dataset used is comprised of microscopic images of red blood cells that were either parasitized or uninfected. The infected cells were classified based on two broad categories: the type of malarial parasite causing the infection and the stage of progression of the disease. The dataset was manually annotated using the LabelImg tool. The images were then augmented to enhance model training. Both models YOLOv5 and scaled YOLOv4 proved effective in classifying the type of parasite. Scaled YOLOv4 was in the lead with an accuracy of 83% followed by YOLOv5 with an accuracy of 78.5%. The proposed models may be useful for the medical professionals in the accurate diagnosis of malaria and its stage prediction.

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Section: Parasite Classificationmentioning

confidence: 99%

Classification of Malaria Using Object Detection Models

et al. 2022

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“…A study proposing a technique to find out the perfect area of blood cells for determining cell composition by using image segmentation. Region Growing was used to find the area of the complete blood cell [7]. The study of Sanaha S. Pathan and Avinash D. Harale proposed the use of image processing for counting silkworm eggs.…”

Section: Introductionmentioning

confidence: 99%

Counting and Separating Damaged Seeds of Soybean Seeds using Image Processing

Wattana

Siriluk

Khotwit

2018

International Journal on Advanced Science, Engineering and Information Technology

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This research proposed an image processing technique for counting soybean seeds and separating damaged seeds. The technique used was to adjust the image to black and white so that the soybean seeds differ from the background color. The second part, of the research, was the separation of soybean seeds using the distance transform method and the region growing method, to count soybean seeds. In the third part, it focused on the separation of damaged seeds by the size, the circle shape and HSV of soybean seeds. From 30 soybean seed images, the percentage of accuracy of counting and separating damaged seeds by naked eyes was 100 percent, and the average time spent was 13.70 seconds. The percentage of accuracy of counting the seeds by the developed program was 100 percent, and the accuracy of the separation of damaged seeds was 99.80 percent. The average time spent was 6.49 seconds. The experimental results showed that the developed program took 2 times less to count the soybean seeds than the naked eyes. Therefore, the proposed algorithm of the program can help save the time for counting the soybean seeds and separate the damaged seeds.

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