Malaria Parasite Classification Framework using a Novel Channel Squeezed and Boosted CNN.

Abstract

Malaria is a life-threatening infection that infects the red blood cells (RBCs) that gradually grows throughout the body. The plasmodium parasite is caused by a female anopheles mosquito bite and severely affects numerous individuals within the world every year. Therefore, early detection tests are required to predict infected parasitic cells. The proposed technique exploits deep convolutional neural network (CNN) learning capability to detect the thin-blood smear parasitic patients from healthy individuals. In this regard, the detection is accomplished using a novel STM-SB-RENet block-based CNN that employs the idea of split-transform-merge (STM) and channel Squeezing-Boosting (SB) in a modified fashion. In this connection, a new convolutional block-based STM is developed, which systematically implements region and edge operations to explore the parasitic malaria pattern related to region-homogeneity, structural obstruction, and boundary-defining features. Moreover, the diverse boosted feature maps are achieved by incorporating the new channel SB and Transfer Learning (TL) idea in each STM block at abstract, intermediate, and target levels to capture minor contrast and texture variation between parasitic and normal artifacts. The malaria input images to the proposed models are initially transformed using discrete wavelet transform to generate enhanced and reduced feature space. The proposed architectures are validated using hold-out cross-validation on the National Institute of Health Malaria dataset. The proposed methods outperform the train from scratch, and TL-based fine-tuned existing techniques. The considerable performance (accuracy: 97.98%, sensitivity: 0.988, F-score: 0.980, and AUC: 0.996) of STM-SB-RENet suggests that it can be utilized to screen parasitic malaria patients.