Robust Deep Neural Network Model for Identification of Malaria Parasites in Cell Images

Huq, Aminul; Pervin, Mst. Tasnim

doi:10.1109/tensymp50017.2020.9230832

Cited by 11 publications

(13 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…6. Finally, the proposed DBEL framework's further reduced the FN (34), as contrasted to the best and most reputable DenseNet-201. However, a few samples were missed due to a similarity between malaria-infected and healthy people owing to impurity, stain, or noise anomalies in non-parasitic instances, as seen in Fig.…”

Section: Enhanced Dataset Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework

Khan¹,

Sadiq

Liang

et al. 2022

Preprint

View full text Add to dashboard Cite

Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and millions worldwide yearly. However, specialists' manual screening in clinical practice is laborious and prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL) framework, comprising the stacking of new Boosted-BR-STM convolutional neural networks (CNN) and the ensemble ML classifiers, is developed to screen malaria parasite images. The proposed Boosted-BR-STM is based on a new dilated-convolutional block-based Split Transform Merge (STM) and feature-map Squeezing-Boosting (SB) ideas. Moreover, the new STM block uses regional and boundary operations to learn the malaria parasite's homogeneity, heterogeneity, and boundary with patterns. Furthermore, the diverse boosted channels are attained by employing Transfer Learning-based new feature-map SB in STM blocks at the abstract, medium, and conclusion levels to learn minute intensity and texture variation of the parasitic pattern. The proposed DBEL framework implicates the stacking of prominent and diverse boosted channels and provides the generated discriminative features of the developed Boosted-BR-STM to the ensemble of ML classifiers. The proposed framework improves the discrimination ability and generalization of ensemble learning. Moreover, the deep feature spaces of the developed Boosted-BR-STM and customized CNNs are fed into ML classifiers for comparative analysis. The proposed DBEL framework outperforms the existing techniques on the NIH malaria dataset that are enhanced using discrete wavelet transform to enrich feature space. The proposed DBEL framework achieved Accuracy (98.50%), Sensitivity (0.9920), F-score (0.9850), and AUC (0.9960), which suggest it to be utilized for malaria parasite screening.

show abstract

Section: Enhanced Dataset Evaluationmentioning

confidence: 99%

“…Previous studies employed VGG16 to identify malaria using the common NIH dataset and achieved 95.96% accuracy [34]. The balanced NIH dataset included 27,556 images and was resized to 224 by 224 pixels.…”

Section: Related Workmentioning

confidence: 99%

Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework

Khan¹,

Sadiq

Liang

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Several pre-trained models-AlexNet, VGG16, NasNetMobile, Xception, Inception and ResNet50-were also used for malaria parasite detection using blood-smear images by Sriporn et al [22]. A total of 7000 RGB blood-smear images comprising 4500 infected and 2500 uninfected were used in the study, and data augmentation was applied by rotating images by 90, 180, and 270 degrees to increase the number of the samples while preserving the detailed information.…”

Section: Literature Reviewmentioning

confidence: 99%

DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features

Abubakar

Ajuji

Yahya

2021

ASI

View full text Add to dashboard Cite

Malaria is one of the most infectious diseases in the world, particularly in developing continents such as Africa and Asia. Due to the high number of cases and lack of sufficient diagnostic facilities and experienced medical personnel, there is a need for advanced diagnostic procedures to complement existing methods. For this reason, this study proposes the use of machine-learning models to detect the malaria parasite in blood-smear images. Six different features—VGG16, VGG19, ResNet50, ResNet101, DenseNet121, and DenseNet201 models—were extracted. Then Decision Tree, Support Vector Machine, Naïve Bayes, and K-Nearest Neighbour classifiers were trained using these six features. Extensive performance analysis is presented in terms of precision, recall, f-1score, accuracy, and computational time. The results showed that automating the process can effectively detect the malaria parasite in blood samples with an accuracy of over 94% with less complexity than the previous approaches found in the literature.

show abstract

“…Por otro lado, los enfoques basados en la utilización de redes profundas pre entrenadas se presentan en (Huq y Pervin, 2020;Nakasi et. al, 2021;Var y Boray, 2018;Bharadwaj y Sujitha, 2019;Chakradeo et.…”

Section: Trabajos Relacionadosunclassified

“…al, 2021). En (Huq y Pervin, 2020) se implementó la red VGG16 para identificar imágenes de células con parásitos y sin parásitos. Se utilizó un conjunto de datos disponible en National Institute of Health, que tiene 27558 imágenes disponibles.…”

Section: Trabajos Relacionadosunclassified

Clasificación de parásitos en imágenes de copro con transferencia de aprendizaje y aumento de datos

Velázquez

Murguía

Quintana

et al. 2021

recibe

View full text Add to dashboard Cite

Los seres humanos pueden albergar parásitos, por lo tanto, es fundamental una detección temprana para prevenir enfermedades. Los parásitos pueden observarse a través de imágenes microscópicas, con lo cual, la visión por computadora se muestra como un enfoque que pueda ayudar a la detección y clasificación de parásitos en imágenes digitales. Los modelos de aprendizaje profundo han mostrado un desempeño formidable en la clasificación de imágenes, debido a esto, en este artículo se presentan varios clasificadores profundos multiclase para reconocer 8 clases: 7 tipos de parásitos y la clase no parásitos. Los clasificadores diseñados utilizan transferencia de aprendizaje basada en la arquitectura AlexNet modificada. Al contar con una cantidad de muestras escasa de imágenes de parásitos, se realizó también un aumento de datos, tanto con el método tradicional como mediante la generación de imágenes con una red generativa adversaria (GAN) diseñada para ese propósito. El clasificador con mejor desempeño presentó una exactitud del 99.94%, 98.97% y 98.18% en el conjunto de entrenamiento, validación y de prueba, respectivamente.

show abstract

Robust Deep Neural Network Model for Identification of Malaria Parasites in Cell Images

Cited by 11 publications

References 10 publications

Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework

Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework

DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features

Clasificación de parásitos en imágenes de copro con transferencia de aprendizaje y aumento de datos

Contact Info

Product

Resources

About