Improving Malaria detection using enhanced-efficientnet deep neural network approach

Malaria detection traditionally relies on microscopic examination of blood smears, a process that is labor-intensive and prone to human error. This study aims to introduce a robust automated detection method using deep learning, designed to enhance diagnostic accuracy and reduce human effort. The research presents an innovative Enhanced-EfficientNet (EEN) deep neural network approach comprising three distinct phases: image preprocessing, feature extraction using the Enhanced-EfficientNet model, and classification using a Deep Neural Network (DNN). The proposed methodology was validated using a dataset containing 27,558 labeled blood cell images equally divided between "infected" and "uninfected" samples. The proposed EEN approach achieved superior diagnostic performance, with a maximum classification accuracy of 97.71%, precision of 97.71%, recall of 97.72%, and an F1 score of 97.71% on the test dataset. Comparative evaluation with established models, including VGG16, Xception, ResNet152, EfficientNetB3, and InceptionV3, confirmed significant performance improvements offered by the proposed method. The Enhanced-EfficientNet model effectively addresses the accuracy and reliability challenges associated with traditional malaria diagnostics, presenting a robust deep learning alternative with improved diagnostic outcomes. The study underscores deep learning's practical value as a supportive diagnostic tool, facilitating quicker, more reliable detection of malaria infections. Clinicians can leverage this technology to enhance patient care, significantly reduce diagnostic errors, and improve survival outcomes for patients.