Improved Malaria Cells Detection Using Deep Convolutional Neural Network - Mahmood, S.N.; Mohammed, S.S.; Ismaeel, A.G.; Gökalp, H.G.; Mahmood, I.N.; Aziz, D.A.; Alani, S. (Institute of Electrical and Electronics Engineers Inc., 2023)
This research presents a deep convolutional neural network (CNN) as a solution for identifying malarial cells that are infected. The AI model suggested in this work comprises a three-layered CNN and a two-layered dense neural network. The model can capture both minor and significant features by utilizing CNN, thereby extracting a maximum amount of information from the input data. The model is trained over 20 epochs and evaluated using the binary cross entropy loss function and accuracy metric to assess its performance. Remarkably, the proposed model achieved an impressive accuracy of 96% and maintained a loss value below 0.2 for both the training and validation datasets. Ultimately, this research demonstrates promising potential for automating the detection of malaria through parasite cell counting. © 2023 IEEE.
Keywords: Convolutional Neural Network; Deep Learning; Feature Extraction; Image Segmentation; Malaria; Parasite Classification