The project aims to develop an automated system for detecting malaria-infected cells from microscopic images using Convolutional Neural Networks (CNNs). Malaria is a life-threatening disease caused by parasites transmitted through mosquito bites. Early and accurate diagnosis is crucial for effective treatment and disease control. By leveraging deep learning techniques, we can build a system that can rapidly and accurately classify blood smear images as infected or uninfected, aiding healthcare professionals in diagnosing malaria.
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The project aims to develop an automated system for detecting malaria-infected cells from microscopic images using Convolutional Neural Networks (CNNs). By leveraging deep learning techniques, we can build a system that can rapidly and accurately classify blood smear images as infected or uninfected.
vvidhig/Malarial-Cell-Detection-Model
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The project aims to develop an automated system for detecting malaria-infected cells from microscopic images using Convolutional Neural Networks (CNNs). By leveraging deep learning techniques, we can build a system that can rapidly and accurately classify blood smear images as infected or uninfected.
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