sauravmishra1710/Malaria-Detection-Using-Deep-Learning-Techniques
Malaria Parasite Detection using Efficient Neural Ensembles. Malaria, a life threatening disease caused by the bite of the Anopheles mosquito infected with the parasite, has been a major burden towards healthcare for years leading to approximately 400,000 deaths globally every year. This study aims to build an efficient system by applying ensemble techniques based on deep learning to automate the detection of the parasite using whole slide images of thin blood smears.
This project offers an automated system for malaria diagnosis, helping healthcare professionals quickly identify the parasite in blood smear images. It takes whole slide images of thin blood smears and provides a clear classification of whether malaria parasites are present, making the diagnostic process faster and more consistent. Clinical laboratory technicians, pathologists, and medical researchers can use this to enhance malaria detection.
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Use this if you need a rapid, automated, and highly accurate method for detecting malaria parasites from digital thin blood smear images to support diagnosis.
Not ideal if you require a diagnostic tool for other parasitic diseases or if you only have physical blood smear slides without digital imaging capabilities.
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Dec 12, 2023
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