Artificial Neural Networks for Detection of Malaria in RBCs
This work addresses malaria detection, a critical public health issue, but appears incremental as it applies an existing ANN method to new image data without major methodological innovation.
The researchers tackled malaria diagnosis by using artificial neural networks to classify red blood cells as infected or not based on features from digital holographic images, achieving a classification task but without reporting specific performance numbers.
Malaria is one of the most common diseases caused by mosquitoes and is a great public health problem worldwide. Currently, for malaria diagnosis the standard technique is microscopic examination of a stained blood film. We propose use of Artificial Neural Networks (ANN) for the diagnosis of the disease in the red blood cell. For this purpose features / parameters are computed from the data obtained by the digital holographic images of the blood cells and is given as input to ANN which classifies the cell as the infected one or otherwise.