A Novel Data Augmentation Convolutional Neural Network for Detecting Malaria Parasite in Blood Smear Images

Figures & data

Table 1. Summary of all the related works with other algorithms and contributions.

Ref.	Algorithms Proposed	Contributions	Research Gap
Bibin, Nair, and Punitha 2017	Deep Belief Network (DBN).	The performance of DBN is satisfactory.	The image dataset used for the experiment is very small and there is no guarantee that their technique can handle a large dataset.
Rajaraman, Jaeger, and Antani 2019	Ensemble Convolution Neural Networks (CNN).	Ensemble of VGG-19 and SqueezeNet outperformed other ensemble models	There is a need to further enhance the performance of their proposed technique.
Qanbar and Tasdemir 2019	Residual Attention Network (RAN) and Support Vector Machine (SVM)	RAN perform better than SVM.	The performance of the proposed system was not compared with other state-of-the-art deep learning or machine learning models.
Pan, Dong, and Wu 2018	Deep CNN by using image processing techniques and data augmentation methods.	Data augmentation improved performance.	A further enhancement is necessary.
Chaya and Usha 2019	Cuckoo Search Based Ensemble Classifier (CSEC), Scale to Estimate Premature Malaria Parasites Scope (SEMP), and hybrid classification of malaria blood smear images.	CSEC performs better than Hybrid Classifier in terms of accuracy.	There is no assurance that the metaheuristics used will guarantee a globally optimal solution for some classes of problems.
Mehedi Masud et al. 2020	CNN	CNN has high accuracy in the predictionof malaria blood smear images.	The performance of the proposed system was not compared with other deep learning models.
Kumari, Singh, and Kumar 2019	Logistic Regression, Naive Bayes, KNN, Decision Trees, Random Forest Classifier, SVM, Artificial Neural Network (ANN) with Feature Selection.	Good classification accuracy in prediction of liver disease.	Classification accuracy is the only performance metric used for evaluating the effectiveness of the proposed model. Also, the size of the dataset is small.
Negi, Kumar, and Chauhan 2021	Deep CNN	Good classification accuracy in identification and recognition of plant diseases.	Only accuracy was used as performance metric which is not sufficient to prove the effectiveness of the method. There is no comparison with other deep learning models.
Oyewola et al. 2021.	Deep Residual Convolution Neural Network (DRNN).	High classification accuracy for detection of Cassava Mosaic Disease in cassava leaf images	Overfitting and inability of deep neural network to deal with hostile photographing situations.
Alok, Krishan, and Chauhan 2021.	CNN	High accuracy in detecting malaria.	The performance of the proposed method was not compared with other deep learning models.
Negi and Kumar 2021	CNN	Satisfactory accuracy for the detection and classification of citrus diseases.	Performance of the proposed model is relatively low. Also, the size of the dataset is small. Moreover, the authors did not benchmark their work with other works.

Figure 1. Images of infected and uninfected blood samples.

Figure 2. General architecture of convolutional neural networks.

Figure 3. Directed acyclic graph network.

Figure 4. Schematics of reinforcement learning. The critic is intended to assess the quality of situations $V\left(S\right)$ for the current situation $S\left(t\right)=\left\{\mathrm{\Delta }X\left(t\right),u\left(t\right)\right\}$.

Figure 5. Images of uninfected and infected malaria blood samples.

Table 2. Performance evaluation of models based on MAE, RMSE, and MASE.

MODEL	MAE	RMSE	MASE
CNN	0.313	0.560	1.096
DAGCNN	0.273	0.523	0.956
DACNN	0.0520	0.228	0.181

Table 3. Performance metrics of CNN of malaria blood sample images.

MODEL	${\mathit{S}}_{\mathit{e}}$ (%)	${\mathit{D}}_{\mathit{r}}$ (%)	NPV	Prevalence
Infected	81.24	37.72	80.03	46.43
Uninfected	65.15	34.91	66.89	53.57

Table 4. Performance metrics of DAGCNN of malaria blood sample images.

MODEL	${\mathit{S}}_{\mathit{e}}$ (%)	${\mathit{D}}_{\mathit{r}}$ (%)	NPV	Prevalence
Infected	70.43	43.09	58.53	61.18
Uninfected	65.75	25.53	76.42	38.82

Table 5. Performance metrics of DACNN of malaria blood sample images.

MODEL	${\mathit{S}}_{\mathit{e}}$ (%)	${\mathit{D}}_{\mathit{r}}$ (%)	NPV	Prevalence
Infected	96.32	53.21	95.34	55.24
Uninfected	92.91	41.58	94.37	44.76

Table 6. Overall evaluation of CNN, DAGCNN, and DACNN models based on $A_c,K$, and $CI$.

MODEL	${\mathit{A}}_{\mathit{c}}$ (%)	$\mathit{K}$	95% $\mathit{C}\mathit{I}$
CNN	72.62	45.74	(0.7143, 0.738)
DAGCNN	68.61	35.38	(0.6737, 0.6984)
DACNN	94.79	89.44	(0.9417, 0.9536)

Figure 6. Activations from the third CNN layer of malaria blood sample images.

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