<![CDATA[This study discusses the design and development of a web-based application for malariadisease detection using Computer Vision. The main objective of this research is to developa system that can assist medical personnel in detecting malaria infections more quickly andaccurately by utilizing computer vision technology. The proposed system employs theConvolutional Neural Network (CNN) algorithm to analyze microscopic erythrocyteimages obtained from blood samples. Malaria image data used in this study were collectedfrom various online sources as well as through direct observation in hospitals.The systemdevelopment process begins with the collection of malaria image data, which is thenprocessed through preprocessing stages to enhance data quality and prepare it for modeltraining. Once the data is ready, the CNN model is trained using augmented training datato improve the model's generalization. Model evaluation is conducted using test data tomeasure the accuracy and performance of the model in detecting malaria.Evaluation results indicate that the developed CNN model has high accuracy in detecting malaria infections,with satisfactory precision, recall, and F1-score values. The system is also capable ofgenerating detection reports and visualizations that facilitate medical personnel indiagnosis. This system is expected to support efforts to improve malaria diagnosis inhealthcare facilities, particularly in remote areas with limited access to trained medicalpersonnel. Additionally, the system is anticipated to reduce the workload of medicalpersonnel and increase efficiency in the malaria diagnosis process.]]>
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