Abstract
Malaria is a critical health condition that affects both sultry and frigid region worldwide, giving rise to millions of cases of disease and thousands of deaths over the years. Malaria is caused by parasites that enter the human red blood cells, grow there, and damage them over time. Therefore, it is diagnosed by a detailed examination of blood cells under the microscope. This is the most extensively used malaria diagnosis technique, but it yields limited and unreliable results due to the manual human involvement. In this work, an automated malaria blood smear classification model is proposed, which takes images of both infected and healthy cells and preprocesses them in the L*a*b* color space by employing several contrast enhancement methods. Feature extraction is performed using two pretrained deep convolutional neural networks, DarkNet-53 and DenseNet-201. The features are subsequently agglutinated to be optimized through a nature-based feature reduction method called the whale optimization algorithm. Several classifiers are effectuated on the reduced features, and the achieved results excel in both accuracy and time compared to previously proposed methods.
Highlights
Malaria is a critical and intimidating disease, which has been of great concern for humans over a long period of time [1]
Malaria is the prime cause of thousands of deaths in both warm and cold regions worldwide, and it has reached over 228 million cases and 400 thousand deaths
An attention dense circular net (ADCN) was proposed for infected red blood cell classification. It was inspired by the state-of-the-art convolutional neural network (CNN) models ResNet and DenseNet, which operate on the basis of remnant connections
Summary
Malaria is a critical and intimidating disease, which has been of great concern for humans over a long period of time [1]. The bite of the Anopheles mosquito injects these harmful malaria-causing sporozoites inside the human bloodstream, which carries them to the liver with the normal blood flow process. Solution, stain material, methanol, and immersion oil, which are not easy to afford unless the tests are being carried out in a well-established hospital laboratory These limitations require the formulation of a compact automated system for the detection and diagnosis of malaria in a blood smear, and for the classification of healthy and infected blood cells. An attention dense circular net (ADCN) was proposed for infected red blood cell classification It was inspired by the state-of-the-art convolutional neural network (CNN) models ResNet and DenseNet, which operate on the basis of remnant connections.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
