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 and gradually grows throughout the body. The plasmodium parasite is transmitted by a female Anopheles mosquito bite and severely affects numerous individuals within the world every year. Therefore, early detection tests are required to identify parasite-infected cells. The proposed technique exploits the learning capability of deep convolutional neural network (CNN) to distinguish the parasite-infected patients from healthy individuals using thin blood smear. 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 infection pattern of malaria 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 parasite-infected and normal artifacts. The malaria input images for 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 training from scratch and TL-based fine-tuned existing techniques. The considerable performance (accuracy: 97.98%, sensitivity: 0.988, F-score: 0.980 and area under the curve: 0.996) of STM-SB-RENet suggests that it can be utilized to screen malaria-parasite-infected patients. Graphical Abstract.

Keywords: CNN; classification; malaria parasite; split-transform and merge; squeezing and boosting; transfer learning.