Automated Detection of Malaria-Infected Cells Using Convolutional Neural Networks
Sahil Khan · International Journal for Research in Applied Science and Engineering Technology · 2024
Abstract: In this study, we developed and evaluated a convolutional neural network (CNN) model to detect malaria-infected cells from microscopic images. The dataset comprised 27,558 images categorized into "Infected" and "Uninfected" cells. The model architecture included multiple convolutional layers, max-pooling layers, and dropout layers to enhance feature extraction and prevent overfitting. Data augmentation techniques were employed to improve the model's generalization capability. The CNN model demonstrated high performance, achieving over 90% accuracy on both training and validation datasets. The training and validation accuracy curves showed a rapid increase in the initial epochs, followed by a plateau, indicating the model's robustness. The loss curves revealed a significant decrease in both training and validation loss, with the validation loss stabilizing at a lower level than the training loss due to effective regularization. This model presents a promising approach for accurate and efficient detection of malaria-infected cells, which can aid in timely diagnosis and treatment, ultimately improving patient outcomes.