Abstract:

The goal of this project is to develop a system for the accurate classification of malaria diseases using Convolutional Neural Networks (CNNs) and VGG models. Malaria is a life-threatening disease caused by parasites transmitted to humans through the bites of infected mosquitoes. Early detection and accurate diagnosis are critical in the treatment of the disease. To achieve this goal, we propose the use of image-based diagnosis using CNNs and VGG models. These models have proven to be highly effective in image recognition and classification tasks. We will use a large dataset of malaria images to train our models, and evaluate their performance using various metrics such as accuracy, precision, recall, and F1 score. The proposed system will have several benefits, including improved accuracy and efficiency in malaria diagnosis, reduced workload for medical professionals, and increased access to reliable diagnosis in low resource settings. The system can also be extended to other medical imaging applications, and can serve as a basis for further research in the field of deep learning for medical diagnosis. Overall, the successful implementation of this project will contribute significantly to the fight against malaria, which remains a major global health challenge.

Description:

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