Hybrid classifier based life cycle stages analysis for malaria-infected erythrocyte using thin blood smear images

Devi, Swapna; Laskar, Rabul Hussain; Sheikh, Shah Alam

doi:10.1007/s00521-017-2937-4

Cited by 23 publications

(20 citation statements)

References 45 publications

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“…SVM19 23–25 and KNN16 17 21 26 are among the most frequently used approaches to address the recognition of malaria parasites. In our work, SVM was also the best classifier for the first module.…”

Section: Discussionmentioning

confidence: 99%

“…Segmentation is the first step in machine learning approaches. Usually, it is done from the green component of RGB images, using histogram thresholding techniques14–16 and applying watershed algorithm 17–19. In other works, segmentation is based on granulometry analysis20 or in the use of thresholding from the HSV colour space 21.…”

Section: Introductionmentioning

confidence: 99%

“…Segmentation is followed by feature extraction, which uses colour, textural and geometric morphological information 22. Features are used to train classifiers, which in most cases are based on support vector machine (SVM),19 23–25 k-nearest neighbours (KNN)16 17 21 26 or decision trees algorithms 27. Latest trends in classification of red blood cells (RBC) infected with malaria propose the use of deep learning approaches 14 17 18 25 28.…”

Section: Introductionmentioning

confidence: 99%

“…Features are used to train classifiers, which in most cases are based on support vector machine (SVM),19 23–25 k-nearest neighbours (KNN)16 17 21 26 or decision trees algorithms 27. Latest trends in classification of red blood cells (RBC) infected with malaria propose the use of deep learning approaches 14 17 18 25 28. Literature reveals that the automatic recognition of malaria has been addressed: (1) to discern between infected versus non-infected RBC, (2) to differentiate between Plasmodium species or (3) to discriminate among different parasite stages.…”

Section: Introductionmentioning

confidence: 99%

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Sequential classification system for recognition of malaria infection using peripheral blood cell images

et al. 2020

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AimsMorphological recognition of red blood cells infected with malaria parasites is an important task in the laboratory practice. Nowadays, there is a lack of specific automated systems able to differentiate malaria with respect to other red blood cell inclusions. This study aims to develop a machine learning approach able to discriminate parasitised erythrocytes not only from normal, but also from other erythrocyte inclusions, such as Howell-Jolly and Pappenheimer bodies, basophilic stippling as well as platelets overlying red blood cells.MethodsA total of 15 660 erythrocyte images from 87 smears were segmented using histogram thresholding and watershed techniques, which allowed the extraction of 2852 colour and texture features. Dataset was split into a training and assessment sets. Training set was used to develop the whole system, in which several classification approaches were compared with obtain the most accurate recognition. Afterwards, the recognition system was evaluated with the assessment set, performing two steps: (1) classifying each individual cell image to assess the system’s recognition ability and (2) analysing whole smears to obtain a malaria infection diagnosis.ResultsThe selection of the best classification approach resulted in a final sequential system with an accuracy of 97.7% for the six groups of red blood cell inclusions. The ability of the system to detect patients infected with malaria showed a sensitivity and specificity of 100% and 90%, respectively.ConclusionsThe proposed method achieves a high diagnostic performance in the recognition of red blood cell infected with malaria, along with other frequent erythrocyte inclusions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sequential classification system for recognition of malaria infection using peripheral blood cell images

et al. 2020

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“…The traditional image processing techniques based on image segmentation to identify the target object within an image has a high computational cost and low-performance [47] [26] [46] [57]. The classical machine learning approaches based on manually extracted features for the specified object type had also been widely studied as object classification and detection technique [56] [65] [11]. The limitation of such classical machine learning-based techniques is their inability to cope-up with the inherent variability of images captured in different environments.…”

Section: Introductionmentioning

confidence: 99%

Malaria Parasite Detection in Thick Blood Smear Microscopic Images Using Modified YOLOV3 and YOLOV4 Models

Abdurahman

Anlay

Aliy

2020

Preprint

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Background Information: Manual microscopic examination is still the "golden standard" for malaria diagnosis. The challenge in the manual microscopy is the fact that its accuracy, consistency and speed of diagnosis depends on the skill of the laboratory technician. It is difficult to get highly skilled laboratory technicians in the remote areas of developing countries. In order to alleviate this problem, in this paper, we propose and investigate the state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from thick blood slides. Methods: YOLOV3 and YOLOV4 are state-of-the-art object detectors both in terms of accuracy and speed; however, they are not optimized for the detection of small objects such as malaria parasite in microscopic images. To deal with these challenges, we have modified YOLOV3 and YOLOV4 models by increasing the feature scale and by adding more detection layers, without notably decreasing their detection speed. We have proposed one modified YOLOV4 model, called YOLOV4-MOD and two modified models for YOLOV3, which are called YOLOV3-MOD1 and YOLOV3-MOD2. In addition, we have generated new anchor box scales and sizes by using the K-means clustering algorithm to exploit small object detection learning ability of the models.Results: The proposed modified YOLOV3 and YOLOV4 algorithms are evaluated on publicly available malaria dataset and achieve state-of-the-art accuracy by exceeding the performance of their original versions, Faster R-CNN and SSD in terms of mean average precision (mAP), recall, precision, F1 score, and average IOU. For 608 x 608 input resolution YOLOV4-MOD achieves the best detection performance among all the other models with mAP of 96.32%. For the same input resolution YOLOV3-MOD2 and YOLOV3-MOD1 achieved mAP of 96.14% and 95.46% respectively. Conclusions: Th experimental results demonstrate that the performance of the proposed modified YOLOV3 and YOLOV4 models are reliable to be applied for detection of malaria parasite from images that can be captured by smartphone camera over the microscope eyepiece. The proposed system can be easily deployed in low-resource setting and it can save lives.

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Improved Otsu Algorithm for Segmentation of Malaria Parasite Images

Sangole

Gandhe

Patil

2022

Medical Imaging and Health Informatics

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Hybrid classifier based life cycle stages analysis for malaria-infected erythrocyte using thin blood smear images

Cited by 23 publications

References 45 publications

Sequential classification system for recognition of malaria infection using peripheral blood cell images

Sequential classification system for recognition of malaria infection using peripheral blood cell images

Malaria Parasite Detection in Thick Blood Smear Microscopic Images Using Modified YOLOV3 and YOLOV4 Models

Improved Otsu Algorithm for Segmentation of Malaria Parasite Images

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