Modeling a Novel Hyper-Parameter Tuned Deep Learning Enabled Malaria Parasite Detection and Classification

A theoretical methodology is suggested for finding the malaria parasites’presence with the help of an intelligent hyper-parameter tuned Deep Learning(DL)based malaria parasite detection and classification(HPTDL-MPDC)in the smear images of human peripheral blood.Some existing approaches fail to predict the malaria parasitic features and reduce the prediction accuracy.The trained model initiated in the proposed system for classifying peripheral blood smear images into the non-parasite or parasite classes using the available online dataset.The Adagrad optimizer is stacked with the suggested pre-trained Deep Neural Network(DNN)with the help of the contrastive divergence method to pre-train.The features are extracted from the images in the proposed system to train the DNN for initializing the visible variables.The smear images show the concatenated feature to be utilized as the feature vector in the proposed system.Lastly,hyper-parameters are used to fine-tune DNN to calculate the class labels’probability.The suggested system outperforms more modern methodologies with an accuracy of 91%,precision of 89%,recall of 93%and F1-score of 91%.The HPTDL-MPDC has the primary application in detecting the parasite of malaria in the smear images of human peripheral blood.

Intelligent Deep Transfer Learning Based Malaria Parasite Detection andClassification Model Using Biomedical Image

Malaria is a severe disease caused by Plasmodium parasites,which can be detected through blood smear images.The early identification of the disease can effectively reduce the severity rate.Deep learning(DL)models can be widely employed to analyze biomedical images,thereby minimizing the misclassification rate.With this objective,this study developed an intelligent deep-transfer-learning-based malaria parasite detection and classification(IDTL-MPDC)model on blood smear images.The proposed IDTL-MPDC technique aims to effectively determine the presence of malarial parasites in blood smear images.In addition,the IDTL-MPDC technique derives median filtering(MF)as a pre-processing step.In addition,a residual neural network(Res2Net)model was employed for the extraction of feature vectors,and its hyperparameters were optimally adjusted using the differential evolution(DE)algorithm.The k-nearest neighbor(KNN)classifier was used to assign appropriate classes to the blood smear images.The optimal selection of Res2Net hyperparameters by the DE model helps achieve enhanced classification outcomes.A wide range of simulation analyses of the IDTL-MPDC technique are carried out using a benchmark dataset,and its performance seems to be highly accurate(95.86%),highly sensitive(95.82%),highly specific(95.98%),with a high F1 score(95.69%),and high precision(95.86%),and it has been proven to be better than the other existing methods.