## 2 Related Work
In [12], the support vector machine (SVM) and Bayesian learning are used for the classification of malaria cells by utilizing the discriminative feature set. In the malaria detection algorithm presented in [41], linear Euclidean distance classifier with Poisson distribution and Gabor filtering are used to detect malaria cells in blood smear images. An adaptive neuro-fuzzy interface system (ANFIS) is used to diagnose species of malaria infection in [42]. A genetic algorithm based on chromosome-encoding schemes and mutation strategies are also utilized to diagnose malaria-infected cells [43]. K-means clustering is used for the unsupervised detection of malaria-infected cells in [19]. In [44], SVM and artificial neural network (ANN) detect the malaria parasite by using the information of normalized red, green, and blue, and the texture features that are invariant to staining variations. Deep learning (DL) revolutionized the traditional malaria detection pipeline by skipping the feature selection step that requires expertise to capture variability in the angle, position, shape, texture, color, and size of objects in images. Deep learning became popular for medical image analysis because of its biggest advantage of learning features from underlying data without any designing of feature sets. Deep learning models use non-linear activation units for neurons in layers that discover underlying hierarchical patterns in the data. Features are extracted using end-to-end hidden layers that learn complex decision-making functions, leading to classification. A deep learning CNN model is used by [PERSON] et al. [45] for cell segmentation and deep belief networks are used for the classification of malaria-infected and uninfected images. The CNN models [46] can recognize patterns in microscopic images with a much higher accuracy rate than other traditional approaches. Fully convolutional regression networks (FCRN) [47] regress CNN spatial feature maps to detect and count cells in microscopic images. Deep learning approaches used for cell segmentation provide more accurate results as compared to previous techniques. The convolutional neural networks are used for images by exploiting the information of spatial local correlation between adjacent pixels by using shared weights, local receptive fields, and down-sampling of feature maps. In recent researches, customized CNN with focus stack images showed improvements in classification performance [48]. In [49], a customized CNN model with 16 layers is proposed that outperforms all transfer learning models. The CNN Models AlexNet and VGG-16 are compared in [50] to improve the accuracy of malaria detection. [PERSON] et al. [51] uses two-stage classification with faster RCNN for detecting red blood cells in images and and used AlexNet for the classification of malaria cells. The method in [52] proposed LeNet-5 for automated diagnosis of malaria. The VGG-16 is unified with SVM in [53] to classify _falciparum_ malaria cells. Pre-trained CNN models trained on imagenet weights e.g., VGG, ResNet, etc. are used as feature extractors in [54] and classify malaria-infected and uninfected cells. The malaria detectors presented in [55; 56] also use deep learning. An ensemble of three deep learning models, customized CNN, VGG-16, and CNN with SVM, is proposed in [57] for malaria parasite detection.