the parasite in blood while a thin smear detects species of malaria and parasite stages. An expert microscopist usually takes 20 to 30 min for a careful examination of a single blood film to count the number of infectious cells by inspecting the variations in the shape, color, and size characteristics of red blood cells. Millions of blood smear films are manually examined by expert pathologists every year and it takes a massive human and economic effort to diagnose malaria. Moreover, the parasite counts from blood films should be accurate for a correct diagnosis and classification of disease severity. For example, if malaria cells were not present in a patient but the doctor erroneously prescribed antibiotics, it would unnecessarily cause abdominal pain or nausea to the suspected patient [4]. Diagnosis of malaria should be robust with high sensitivity (less false negatives) towards capturing parasites at all stages of the malaria life-cycle. Correct diagnosis at earlier stages can be helpful for the treatment of malaria in endemic regions where expert pathologists are fewer, and the workload of screening blood films is massive. Automatic malaria detection methods can serve many patients with fast, cost-effective, and accurate diagnoses. Traditional methods of automating the malaria detection process involve complex image-processing techniques with hand-engineered features e.g., shape, color, intensity, size, and texture [5; 6; 7]. In these methods, the red blood cells are detected from microscopic images by using different segmentation techniques. After the selection of appropriate features for red blood cells, a computed set of features is used in the classification of segmented images into infected and uninfected classes. For example, morphological-based approaches are used to segment cell images with structuring elements to enhance the characteristics of red blood cells, such as the roundness of cells, which improves the classification accuracy. In the literature, various methods are adopted for the segmentation, feature extraction, and classification of malaria diagnosis [8]. After analyzing conventional and recent malaria detection methods, it is observed that there is a tradeoff between accuracy and the computational complexity of models, that is, when the accuracy of a model increases, its computational complexity also increase [9]. For example, the computation time of classification by support vector machine (SVM) is faster than a deep neural network, but the accuracy of a deep neural network is found to be higher than SVM. In recent years, deep learning (DL) techniques are exploited for automated malaria diagnosis with appreciable detection rates. Deep learning models eliminate the computation of hand-crafted features, as the hidden layers of deep models extract features automatically by analyzing the data. Deep learning models require large datasets for training neural networks and to improve the accuracy of the model. However, in medical applications like malaria diagnosis, relatively small datasets are available. This is because building an annotated dataset requires input from pathologists that is not readily available. To overcome the paucity of the dataset, recently introduced image augmentation techniques in deep learning models provide better generalization and reduce the over-fitting. Image augmentation increases the dataset by taking the original image and transforming it into multiple images by using transformation techniques such as rotation, shear and translation, thus enabling the model to achieve higher accuracy. A convolutional neural network (CNN) is widely used for classification tasks and it is computationally efficient too [10]. In this paper, we evaluate the effectiveness of various existing deep learning models for malaria detection from microscopic blood images, and also propose an efficient DL method for the classification of infected and uninfected malaria cells. The proposed customized CNN-based algorithm outperforms all observed deep learning models.