## 2 Literature Review
The standard accuracy and adversarial accuracy achieved were 95.96% and 29.40% respectively, in which the study showed that the model was fooled by the perturbation introduced. In the second phase of the training (i.e., adversarial training), adversarial images were incorporated into the training dataset and the algorithm was able to learn and classify adversarial images (testing split) more effectively, achieving up to 93.38% accuracy, allowing the model to learn robustly and to stand against such perturbation. It also achieved a 95.79% accuracy on standard images. In a similar development, research by [PERSON] et al. [3] was conducted to classify 27,558 blood-smear images in which 13,779 contained malaria parasites and 13,779 were uninfected using both deep learning from scratch and a transfer learning approach. A CNN model trained from scratch contained 3 convolution layers with a max-pooling layer after each layer, 2 FC layers, and ReLU activation. The CNN model had an input shape of 100 \(\times\) 100 \(\times\) 3 and all convolution layers had a filter size of 3 x 3. The second approach of that study used a certain specific layer which according to the authors provides strong discriminatory features. These pre-trained models included AlexNet, VGG16, ResNet50, Xception, and DenseNet121, and the corresponding feature layers were fc6, block5_conv2, res5c_branch2c, block14_sepconv1, and conv_16_x2, respectively. The results showed that the trained CNN model achieved a classification accuracy of 92.7% and for the transfer learning approach, ResNet50 achieved the best recognition accuracy of 95.9% with a sensitivity of 94.7%. Another study by [PERSON] et al. [20] proposed the use of ensemble learning using three pre-trained models (VGG19, SqueezeNet, and InceptionResNet-V2) and a custom-trained CNN model. Features of those four models were put together before final classification to discriminate images that are infected with the malaria parasite and those that are healthy. In addition, the pre-trained models were also trained independently, and a comparison was made. The result showed that VGG19 achieved a 99.32% accuracy with a sensitivity of 99.31%, outperforming all other pre-trained models including the ensemble method, which recorded an accuracy of 99.11% with a sensitivity of 98.94%  
A study by [PERSON] et al. [21] used a pre-trained ResNet50 model to classify 27,558 blood-smear images for malaria detection. These images were evenly distributed into infected and non-infected images. The approach in this study was the removal of the top-most (classification) layer which was originally trained to classify 1000 classes of objects. In the new scenario, a classification layer with 2 output neurons was added to solve the binary problem, while the earlier (lower) layer of the ResNet50 was frozen allowing them only to contribute features towards training the newly added layer at the top. The new model was compiled and trained using Stochastic Gradient Descent and recorded a validation accuracy of 95.4%. Several pre-trained models--AlexNet, VGG16, NasNetMobile, Xception, Inception and ResNet50--were also used for malaria parasite detection using blood-smear images by [PERSON] et al. [22]. A total of 7000 RGB blood-smear images comprising 4500 infected and 2500 uninfected were used in the study, and data augmentation was applied by rotating images by 90, 180, and 270 degrees to increase the number of the samples while preserving the detailed information. Two activation functions (ReLU and Mish), along with three different optimizers (RMSprop, Nadam, and SGD), were tested with each model. The results showed that the Xception model with the Nadam optimizer and Mish activation function achieved the best detection accuracy of up to 98.8%.