Detection of Malaria Parasite in Giemsa Blood Sample Using Image Processing

Roy, Kishor; Sharmin, Shayla; Mukta, Rahma Bintey Mufiz; Sen, Anik

doi:10.5121/ijcsit.2018.10105

Cited by 14 publications

(4 citation statements)

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“…It was discovered that the model significantly improved prediction ACC without requiring any extra complicated calculations to be performed. Roy et al [28] used image processing to detect malaria parasites in blood smear microscope images. This was done by using a model that used a colour pixel-based discriminating method and a segmentation technique.…”

Section: Related Workmentioning

confidence: 99%

Deep learning-based computer assisted detection techniques for malaria parasite using blood smear images

2023

IJATEE

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Malaria remains a significant global health concern, impacting various regions worldwide. Achieving effective treatment and reducing mortality rates hinges on early and accurate diagnosis. In the year 2021, the World Health Organization (WHO) reported a staggering 619,000 deaths attributed to malaria. Additionally, approximately 214 million individuals were afflicted by this disease during that period. Hence, this study introduces two distinct deep-learning algorithms tailored for malaria disease classification. The first method employs a binary classifier convolutional neural network (CNN) model, attaining an accuracy (ACC) of 90.20%. The second method introduces a customized CNN model that exhibits even greater ACC, reaching an impressive 96.02%. These advanced deep learning (DL) techniques hold the potential to enhance the precision (PRE) and efficiency of malaria diagnosis, ultimately facilitating early disease detection. The study provides comprehensive insights into the proposed models. Model 1 involves malaria disease classification employing a CNN-based binary classifier, while Model 2 adopts a customized CNN architecture. The methodology section elucidates the details of these models, their design, and the execution of experiments undertaken to evaluate their performance. Notably, the proposed method is juxtaposed with the state-of-the-art approach, demonstrating superior results in accurately discerning infected and uninfected malaria blood cell images.

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Section: Related Workmentioning

confidence: 99%

Deep learning-based computer assisted detection techniques for malaria parasite using blood smear images

2023

IJATEE

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“…The author assessed the performance of prominent deep learning models, such as ResNet50, AlexNet, FastAI V1, VGG-16 and DenseNet121. Roy et al (2018) detected the malaria parasite in the Giesma blood sample using image processing, where they developed a model that used the color pixel-based discrimination method and a segmentation operation to identify malarial parasites in microscopic blood smear images [13]. Their methodology involved using two different segmentations: watershed segmentation and HSV (hue, saturation, and value space) segmentation.…”

Section: Literature Reviewmentioning

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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“…Red blood cells (RBCs) and Plasmodium parasites are stained with Giemsa. A staining object is required to detect Plasmodium parasites [5].…”

Section: Introductionmentioning

confidence: 99%

Malaria Blood Cell Image Classification using Transfer Learning with Fine-Tune ResNet50 and Data Augmentation

Muhandisin¹,

Azhar²

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Based on the WHO Report related to malaria, it is estimated that there will be 241 million malaria cases and 627,000 deaths from this disease globally in 2020 with the number of deaths increasing yearly. Preventing malaria disease conditions is through early detection. A more quick and precise malaria diagnosis method was required to simplify and reduce the detection process. Medical image classification could be carried out rapidly and precisely using machine learning or deep learning techniques. This research aims to diagnose malaria by classifying images of malaria blood cells using Deep Learning with a Transfer Learning approach. By utilizing various fine-tuning procedures and implementing data augmentation proposed method develops the method from previous studies. Two types of models Frozen ResNet50 and Fine-Tune ResNet50 are being tested. The dataset utilized will be augmented to improve model performance. This study makes use of the "NIH Malaria Cell Images Dataset" a dataset that contains a total of 27,660 image data. It is divided into two classes: parasitized and uninfected. The results are improved from previous research using the fine-tuned VGG16 model with an accuracy of 96% compared to this study using the fine-tuned ResNet50 model which achieved an accuracy score of 98%.

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Detection of Malaria Parasite in Giemsa Blood Sample Using Image Processing

Abstract: Malaria is one of the deadliest diseases ever exists in this planet

Cited by 14 publications

References 1 publication

Deep learning-based computer assisted detection techniques for malaria parasite using blood smear images

Deep learning-based computer assisted detection techniques for malaria parasite using blood smear images

Classification of Malaria Using Object Detection Models

Malaria Blood Cell Image Classification using Transfer Learning with Fine-Tune ResNet50 and Data Augmentation

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