Efficient deep learning-based approach for malaria detection using red blood cell smears

• Published in: Scientific reports Vol. 14; no. 1; pp. 13249 - 16
• Main Authors: Mujahid, Muhammad, Rustam, Furqan, Shafique, Rahman, Montero, Elizabeth Caro, Alvarado, Eduardo Silva, de la Torre Diez, Isabel, Ashraf, Imran
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 10.06.2024; Nature Publishing Group UK; Nature Portfolio
• Subjects: Aquatic insects; Blood; Deep Learning; Disease detection; EfficientNet; Erythrocytes; Erythrocytes - parasitology; Humans; Learning algorithms; Machine learning; Malaria; Malaria - blood; Malaria - diagnosis; Malaria - parasitology; Malaria detection; Parasites; Renal failure; Seizures; Sweating; Transfer learning; Vector-borne diseases; EfficientNet; Malaria detection; Transfer learning; Disease detection
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-63831-0

Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff.