## 5 Experimental Evaluations and Results
### Performance Comparison with Existing Methods  
We also compare the performance of the proposed CNN model with other malaria detectors to evaluate its effectiveness. The compared methods include state-of-the-art and widely accepted automated malaria detection algorithms, including [11; 12; 51; 52; 53; 54; 55; 56]. The malaria detection algorithm proposed by [PERSON] et al. [11] uses adaptive thresholding technique with a morphological image-processing tools. Specifically, object contours and the eight-connected rule are exploited to confirm the existence of malaria parasites in the cell. The malaria detector presented by [PERSON] et al. in [12] uses a marker-controlled \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **Specificity** & **Sensitivity** & **Precision** & **Accuracy** & **F1 Score** & **MCC** & \(\kappa\) \\ \hline VGG-16 & 0.9632 & 0.9538 & 0.9585 & 0.9585 & 0.9585 & 0.9170 & 0.9170 \\ VGG-19 & 0.9655 & 0.9529 & 0.9593 & 0.9592 & 0.9592 & 0.9185 & 0.9185 \\ Xception & 0.9753 & 0.9255 & 0.9508 & 0.9494 & 0.9494 & 0.9002 & 0.9898 \\ Densenet-121 & 0.9526 & 0.9378 & 0.9453 & 0.9452 & 0.9452 & 0.8905 & 0.8904 \\ Densenet-169 & 0.9436 & 0.9327 & 0.9382 & 0.9382 & 0.9382 & 0.8764 & 0.8764 \\ Densenet-201 & 0.8769 & 0.9399 & 0.9079 & 0.9054 & 0.9052 & 0.8132 & 0.8106 \\ Inception\_v3 & 0.9313 & 0.9297 & 0.9306 & 0.9306 & 0.9306 & 0.8611 & 0.8611 \\ Inc. Resnet\_v2 & 0.9662 & 0.9556 & 0.9539 & 0.9539 & 0.9539 & 0.9179 & 0.9179 \\ Resnet-50 & 0.9727 & 0.9237 & 0.9536 & 0.9517 & 0.9617 & 0.9054 & 0.9035 \\ Resnet-101 & 0.9710 & 0.9419 & 0.9566 & 0.9562 & 0.9562 & 0.9129 & 0.9124 \\ Resnet-152 & 0.9726 & 0.9215 & 0.9525 & 0.9505 & 0.9505 & 0.9031 & 0.9011 \\ Squeeze Net & 0.9562 & 0.9311 & 0.9438 & 0.9435 & 0.9435 & 0.8874 & 0.8871 \\ Proposed & **0.9778** & **0.9633** & **0.9682** & **0.9682** & **0.9682** & **0.9364** & **0.9364** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of the proposed method and other deep learning models on NIH Malaria dataset. The best results in each metric are marked in bold. watershed approach to segment the erythrocytes. Various features describing the shape, size, and texture characteristics of segmented erythrocytes are computed and classified with a support vector machine. In [PERSON] [51] algorithm, a faster, region-based convolutional neural network (Faster R-CNN) is proposed for malaria parasite detection. The model is pre-trained on ImageNet and is fine-tuned on the malaria dataset. [PERSON] [55] also uses a deep convolutional neural network for malaria parasite detection. [PERSON] et al. [52] proposed a deep convolutional neural-network-based algorithm for malaria detection. It uses [PERSON]'s method and morphological operations for RBC segmentation, and LeNet-5 [81]-based CNN architecture is used for cell classification. A transfer learning based approach is presented by [PERSON] et al. [53] for identifying malaria-infected cells in microscopic blood images. The presented model is a unification of VGG and support vector machine. The malaria detection algorithm introduced by [PERSON] et al. [54] consists of a three-layer convolutional neural network. It is a fully connected sequential architecture. A deep belief network-based malaria detection approach is proposed by [PERSON] et al. [56]. The performance achieved by the proposed and the compared methods is presented Table 4. The datasets used in some of the compared methods were different from the test dataset, and therefore such a comparison might not be just; nonetheless, it can help to assess the effectiveness of the proposed method. The results show that our method performs better than the compared methods in all performance metrics except sensitivity, where the Das [12] algorithm performs better; however, its specificity and accuracy are considerably lower than the proposed method. The performance results of Rajaraman, Fatima, and proposed algorithms are computed over the same dataset.