💡[Feature]: Malaria Cell Image Classification using Deep Learning

[sreevidya-16]

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Feature Description

• Deep learning techniques, particularly convolutional neural networks (CNNs), are extensively used for malaria cell image classification.
• By training on a large dataset of labeled cell images, these models can automatically learn and extract intricate features that distinguish infected cells from healthy ones.
• This automated approach enhances the accuracy and speed of malaria diagnosis, reducing the reliance on manual microscopy and enabling timely and effective treatment, especially in resource-limited settings.

@TAHIR0110 , @Avdhesh-Varshney could you please assign me this issue under GSSOC'24

Use Case

One practical use case of malaria cell image classification using deep learning is in remote and resource-limited healthcare settings. In these areas, access to skilled microscopists for diagnosing malaria is often limited. By deploying a deep learning-based mobile or desktop application, healthcare workers can capture images of blood smears using a microscope attached to a smartphone.

Benefits

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Priority

High

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[sreevidya-16]

@TAHIR0110, @Avdhesh-Varshney, could you please assign me this issue under GSSCO'24