Malaria Infection Detection Using Image Processing and Deep Learning Method

Abstract

Malaria remains a critical global health challenge, particularly in developing regions, contributing to substantial morbidity and mortality worldwide. Timely and accurate diagnosis is essential for effective treatment and disease control. This thesis presents a novel approach to malaria detection using advanced image processing and deep learning methodologies. Specifically, convolutional neural networks (CNNs) were applied to the analysis of microscopic blood smear images, enabling precise identification of malaria parasites. To overcome the limitation of small datasets, extensive image augmentation techniques were employed to expand the dataset artificially, increasing its diversity and enhancing the CNN model’s generalization and performance. The augmented dataset was used to train the CNN models, which were evaluated through various performance metrics, including accuracy, recall, precision, and F1-score. Additionally, the confusion matrix, receiver operating characteristic (ROC) curve, precision-recall curve, and learning curves were utilized to demonstrate the efficacy of the proposed method. The CNN-based deep learning architecture was fine-tuned with parameters such as 100 epochs and a batch size of 128 for 64x64 image inputs. The model achieved an accuracy of approximately 97.57%, precision is 97.12%, recall is 95.84%, and the F1-Score is approximately 96.48%. and an AUC-ROC score of 99%, indicating exceptional capability in distinguishing between parasitized and uninfected samples. This research underscores the potential of integrating image processing with deep learning for automated malaria diagnosis, offering a robust and efficient detection system suited for resource-constrained environments. By contributing to global malaria control efforts, this work advances the field of medical diagnostics and paves the way for future applications of deep learning in detecting other infectious diseases.