Table 1 in An optimised YOLOv 4 deep learning model for efficient malarial cell detection in thin blood smear images

Table 1 Summary of recent deep learning approaches in automated malaria diagnostic systems AuthorDatabasePlasmodium speciesClassificationTechniqueResultsSriporn et al. [18]NLM, 7000 cell imagesP.falciparumBinaryXception,Inception-V3,ResNet-50,NasNetMobile,VGG-16, AlexNetBest performing model: Xception Accuracy: 99.28% Precision: 99.29% Recall: 99.29% F1-Score: 99.28%Umer et al. [19]NLM, 27558 cell images (150 infected, 50 healthy patients)P.falciparumBinaryCustomised CNNAccuracy: 99.96% Precision: 100% Recall: 99.93%Zhao et al. [20]NLM, 27558 cell images (150 infected, 50 healthy patients) Broad Institute dataset contains 1364 blood smear images with 80,000 infected cellsP.falciparum P.vivaxBinaryResNet50V2,VGG16, VGG19, InceptionV3,DenseNet121, MobileNetV2Best performing model: VGG-16 Accuracy: 96.53% Sensitivity:95.0% Specificity:98.07% AUC: 99.40% F1-Score: 96.48% MCC: 93.30% Cross-dataset: AUC:94.5%Ragb et al. [21]NLM, 27,558 cell images (150 infected, 50 healthy patients)P.falciparumBinarySqueezeNet,MobileNetV2, GoogleNet, ResNet18, DarkNet19, InceptionV3, AlexNet,Xception, AlexNet, DenseNet201, ResNet101, VGG19, Ensembled modelBest performing model: Ensembled model Sensitivity:97.94% Specificity:97.78% Precision: 97.8%Cinar et al. [22]NLM, 27558 cell images (150 infected, 50 healthy patients)P.falciparumBinaryAlexNet,ResNet50, DenseNet201,VGG19, GoogleNet, InceptionV3Best performing model: DenseNet201 Accuracy: 97.83%Maqsood et al. [23]NLM, 27558 cell images (150 infected, 50 healthy patients)P.falciparumBinaryVGG16,VGG19, Xception, Densenet121, Densenet169, Densenet201, Inceptionv3, Inception-Resnet_v2, Resnet50, Resnet101, Resnet152, SqueezeNet,Customised CNNSpecificity:97.78% sensitivity:96.33% Precision: 96.82% Accuracy: 96.82% F1-Score: 96.82% MCC: 93.64%Diyasa et al. [24]NLM, 27,558 cell images (150 infected, 50 healthy patients)P.falciparumBinaryGoogleNetAccuracy:93.89%Loddo et al. [25]NLM, 27558 cell images (150 infected, 50 healthy patients) MP-IDB,229 imagesP.falciparum P.vivaxBinary and Multi-classAlexNet,DenseNet-201, ResNet-18, ResNet-50, ResNet101, GoogleNet, ShuffleNet, SqueezeNet,MobileNetV2, Inceptionv3,VGG-16Best performing models Binary:ResNet-18 Accuray: 97.68% Multi-class: DenseNet-201: Accuracy: 99.40% Cross-dataset validation: Accuracy: 97.45%Shambhu et al. [26]NLM, 27558 cell images (150 infected, 50 healthy patients)P.falciparumBinaryCustomised CNN96.02%Vijayalaskhmi et al. [27]1030 infected images and 1520 non-infected imagesP.falciparumBinaryLeNet-5, AlexNet,GoogleLeNet,VGG16,VGG19Best performing model: VGG19 Sensitivity:93.44% Specificity: 92.92% Precision: 92.92% Accuracy: 93.13% F1-Score: 93.13%Arshad et al. [28]IML-malariaP.vivaxMulti-classVGG16,VGG19, ResNet50V2, DenseNet169, DenseNet201Best performing model: ResNet-50v2: 79.61%Rahman et al. [29]BBBC041V1:1364 images MP-IDB:229 imagesP.falciparum P.vivaxBinaryVGG-16,VGG-19, Xception, ResNet-50, customised CNNBest performing model: VGG19 Accuracy: 99.35% F1-Score: 96.85% Sensitivity:92.31% Specificity:99.76% AUC: 96.03% Cross Dataset validation: Accuracy: 85.18% Sensitivity:70.19% Specificity:100% F1-Score: 84.82% AUC: 85.09%Yang et al. [30]2567 thin blood smear imagesP.vivaxBinaryYOLOv279.22%Krishnadas et al. [31]MP-IDBP.falciparum P.vivax P.malariae P.ovaleMulti-classYolov5 and Scaled Yolov4Best performing model: Scaled Yolov4 Parasite classification: Accuracy: 83%Sukumarran et al. [32]MP-IDB and Malaria Research Centre, Unimas SarawakP.falciparum P.vivax P.malariae P.ovale P.knowlesiBinaryYOLOv4,Faster R-CNN, SSD-300Best performing model: YOLOv4 mAP: 93.87% Cross dataset: mAP: 84.04% BBBC Broad Bioimage Benchmark Collection, CNN convolutional neural network, MP-IDB Malaria Parasite Image Database, NLM National Library of Medicine, YOLO You Only Look Once object detection algorithm