## 2 Literature Review  
The latest trend that has boosted the performance of many non-medical domains is the use of deep learning. Deep learning is an extension of a well-known multilayer neural network that automatically learns complex data representations, also known as features. However, deep-learning models, unlike humans, require a huge database of quality and annotated data to learn and make an effective decision on future occurrences. This, perhaps, is one of the reasons the medical domain has been unable to adopt the new technology during its early proliferation because is harder to obtain annotated training sets and many privacy concerns arise. Interestingly, the trained deep-learning models can be used to solve problems in the different but similar applications via an approach known as transfer learning. These trained deep-learning models are better known as pre-trained models, which can be described as a model that has already learned to solve a similar problem as the intended one. Transfer learning is one of the three ways to use deep-learning techniques. Other techniques include training the deep-learning model from scratch and fine-tuning the existing deep-learning models [4]. Examples of pre-trained deep-learning models include the VGG16, VGG19, ResNet50, ResNet101, ResNet152, DenseNet121, DenseNet201, AlexNet, and Xception models. Deep learning has been applied in the medical field to address various problems such as face recognition [5; 6; 7], effective classification of skin burns [8; 9; 10; 11; 12], and cancer diagnosis [13; 14; 15], as well as in financial fraud detection [16; 17]. Interestingly, a similar approach was adopted recently to discriminate between blood-smear images that include the Plasmodium parasite and those that do not. In [18], the authors proposed the use of deep-learning techniques for the diagnosis of malaria diseases. This was used basically to discriminate a blood-smear sample that contains malaria and those samples that are negative. During the investigation, two well-known approaches were used: training from scratch and transfer learning approaches using 27,578 RGB images comprising infected and uninfected samples in the ratio 1:1. All images were resized into 44 \(\times\) 44 pixels. In the first approach, 16-layered Convolutional Neural Network (CNN) architecture was used with 6 convolution layers and 3 fully connected layers. The first and the second convolution layers had 32 filters each and they were followed by the max-pooling layer. The third and the fourth convolution layers had 64 filters each and were followed by the average pooling layer, and the fifth and sixth convolution layers had 128 and 256 filters, respectively. Lastly, the architecture of the CNN model was followed by three fully connected layers each with 256 feature neurons. The model was trained with 90% of the images and tested using the remaining 10% of the unseen image samples, and achieved 97.37% accuracy. In the second approach, a pre-trained CNN model (i.e., AlexNet) was used for feature extraction and SVM for classification. This transfer learning approach achieved a 91.99% accuracy  
In another development, a study by [PERSON], [PERSON], et al. [19] used 27,558 images that consisted of 13,779 normal images and 13,779 malaria-infected images obtained from the National Institute of Health. Though all the images were of variable sizes, and all have three channels, they were all rescaled to 224 \(\times\) 224 to match the input shape of the VGG16 pre-trained model used. In addition, as part of the data preparation before the training, the data were split into three: 70% of the data from each class was labeled as training data, 30% was for validation, and 10% was taken out of the training data as testing split. The training data further shrank to 60%. Two experiments were conducted: a standard one and adversarial pieces of training. In the standard training, original images were used, and during testing, and thereafter, some adversarial images were introduced or added into the testing images.