Malaria is a mosquito-borne disease, claiming over 400,000 lives each year, mainly children under 5. By targeting the water bodies where mosquitoes lay eggs, the disease can be controlled or even eliminated completely. Explore more datasets to understand the problem well and come up with a solution to curb this in a sustainable manner.
This project allows the user to upload an image and make predictions on whether it is infected or uninfected.The prediction is made using the model_predict function, which loads the image, reshapes it, and passes it through the ML model to generate a prediction. The predicted class is then returned as a string, which is either "Infected" or "Uninfected".This project has been implemented by ensembling machine learning (image classification) with Flask ,frontend and backend to detect malaria cells
A Malaria Cell Image Classifier using AI-ML has the potential to impact healthcare and government in several ways:
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Early Detection: Automated image classification can help healthcare professionals detect malaria early, leading to more effective treatment.
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Increased Efficiency: Automated classification can reduce the time and resources required to manually classify images, freeing up healthcare professionals to focus on other critical tasks.
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Improved Accuracy: Automated classification can reduce the likelihood of human error, leading to more accurate diagnoses and better treatment outcomes.
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Data Collection: Automated image classification can provide valuable data on the prevalence of malaria, helping healthcare professionals and government agencies develop more effective intervention strategies.
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Cost Savings: Automated image classification can reduce the cost of diagnosing and treating malaria by reducing the time and resources required for manual image classification.
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Better Monitoring and Evaluation: Automated image classification can support better monitoring and evaluation of malaria interventions, enabling healthcare professionals and government agencies to assess the impact of their efforts and make more informed decisions.
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Easy To Use
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Accuracy of 95%+
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Quick Response
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Hosted using Replit : https://malaria-detector.aryanchourey4.repl.co/
Link for Replit: https://replit.com/@aryanchourey4/Malaria-Detector?v=1
Updated Links : https://malaria-detector-2.aryanchourey4.repl.co/
https://replit.com/@aryanchourey4/Malaria-Detector-2?v=1
(H5 file is more than 25mb so couldn't upload it here. Check the Replit for it.)