An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis

Loddo, Andrea; Fadda, Corrado; Ruberto, Cecilia Di

doi:10.3390/jimaging8030066

Cited by 18 publications

(11 citation statements)

References 43 publications

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“…In Table 8 , the proposed classification method results are contrasted with the most recent works published on the same benchmark dataset. In which the pre-trained ResNet-18 model is used for malaria classification it gives an accuracy of 98.68% ( 46 ). The classifiers SVM, neural network, and XG-boost are used for malaria classification with the accuracy of 94, 90, and 80%, respectively ( 47 ).…”

Section: Resultsmentioning

confidence: 99%

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

Amin

Sharif

Mallah

et al. 2022

Front. Public Health

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Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far.

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Section: Resultsmentioning

confidence: 99%

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

Amin

Sharif

Mallah

et al. 2022

Front. Public Health

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“…Every image in MP-IDB is saved in JPG format, with a resolution of 2592 × 1944 pixels and a color depth of 24 bits, with a total file size of about 717 MB. This data collection was obtained solely from thin blood smears stained with Giemsa, as discussed by Loddo et al [ 25 ]. These data were used to perform the models’ training (80%) and testing (20%).…”

Section: Methodsmentioning

confidence: 99%

“…The robustness of models on images infected by various malaria parasite species and domains is eventually called into question by these validations. Studies that have carried out cross-dataset validation [ 25 , 29 ] demonstrate how important it is to examine the generalisation of the model on a separate dataset.…”

Section: Introductionmentioning

confidence: 99%

An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Sukumarran,

Hasikin,

Khairuddin

et al. 2024

Parasites Vectors

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Background Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease’s spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cells, thereby facilitating timely treatment and reducing the risk of disease transmission. This study aims to introduce a more lightweight and quicker model—but with improved accuracy—for diagnosing malaria using a YOLOv4 (You Only Look Once v. 4) deep learning object detector. Methods The YOLOv4 model is modified using direct layer pruning and backbone replacement. The primary objective of layer pruning is the removal and individual analysis of residual blocks within the C3, C4 and C5 (C3–C5) Res-block bodies of the backbone architecture’s C3-C5 Res-block bodies. The CSP-DarkNet53 backbone is simultaneously replaced for enhanced feature extraction with a shallower ResNet50 network. The performance metrics of the models are compared and analysed. Results The modified models outperform the original YOLOv4 model. The YOLOv4-RC3_4 model with residual blocks pruned from the C3 and C4 Res-block body achieves the highest mean accuracy precision (mAP) of 90.70%. This mAP is > 9% higher than that of the original model, saving approximately 22% of the billion floating point operations (B-FLOPS) and 23 MB in size. The findings indicate that the YOLOv4-RC3_4 model also performs better, with an increase of 9.27% in detecting the infected cells upon pruning the redundant layers from the C3 Res-block bodies of the CSP-DarkeNet53 backbone. Conclusions The results of this study highlight the use of the YOLOv4 model for detecting infected red blood cells. Pruning the residual blocks from the Res-block bodies helps to determine which Res-block bodies contribute the most and least, respectively, to the model’s performance. Our method has the potential to revolutionise malaria diagnosis and pave the way for novel deep learning-based bioinformatics solutions. Developing an effective and automated process for diagnosing malaria will considerably contribute to global efforts to combat this debilitating disease. We have shown that removing undesirable residual blocks can reduce the size of the model and its computational complexity without compromising its precision. Graphical Abstract

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“…Artificial Neural Networks (ANN) have been used to make fast and accurate Malaria's diagnoses employing public datasets [10], [11]. These approaches for malaria blood smear detection, segmentation, and classification use or modify the conventional CNN to classify images of infected red blood cells.…”

Section: Recently Numerous Deep Learning Approaches Based Onmentioning

confidence: 99%

Classifying Parasitized and Uninfected Malaria Red Blood Cells Using Convolutional-Recurrent Neural Networks

et al. 2022

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This work aims to classify malaria infected cells from those uninfected using two deep learning approaches. Plasmodium parasite transmitted by a female anopheles's mosquitoes bite is the main cause of malaria. Commonly, Microbiological analyses by a microscope allows detecting cells infected from a blood sample, followed by an specialist interpretation of results to conclude the diagnosis process. Taking advantage of efficient deep learning approaches applied in computer vision field, the present framework propose two deep learning architecture based on Recurrent neural Networks to detect accurately malaria infected cells. The first one implements a Convolutional Long Short-Term Memory while the second uses a Convolutional Bidirectional Long Short-Term Memory architecture. A malaria's public dataset consisting of parasitized and uninfected cell images was used for training and testing the proposed model. The methods developed in this work achieved an accuracy of 99.89 % in the detection of malaria cells infected, without preprocessing data. INDEX TERMS Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Malaria 14 inal and chest pain, and the death [2], [3]. Untreated malaria 15 patients may develop long-term pneumonia, anemia, yellow 16 fever, respiratory or brain disorders (Cerebral Malaria) [4].17The World Health Organization (WHO) is deploying 18 strategies for the prevention, treatment, elimination, and 19 surveillance of Malaria to face this global pandemic [1], 20 starting with the diagnosis of the disease. Basically, micro-21 scopic diagnosis method detecting Malaria consist in a mi-22 crobiological analyses using peripheral blood slides [5], [6]. 23 Collection of blood smears for manual microscopic analysis 24 remains an effective method in the diagnosis of Malaria, 25 compared to other methods such as polymerase chain reac-26 VOLUME 4, 2016

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An Empirical Evaluation of Convolutional Networks for Malaria Diagnosis

Cited by 18 publications

References 43 publications

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification

An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Classifying Parasitized and Uninfected Malaria Red Blood Cells Using Convolutional-Recurrent Neural Networks

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