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 based on the ResNet50 architecture%2C leveraging transfer learning to enhance performance. The Malaria Cell Images Dataset from Kaggle%2C containing 27%2C558 images categorized into infected and uninfected cells%2C was used for training and evaluation. Our model demonstrated high accuracy%2C precision%2C and recall%2C indicating its potential as a reliable tool for assisting in malaria diagnosis. Additionally%2C a web application was developed using Streamlit to allow users to upload cell images and receive predictions about malaria infection%2C making the technology accessible and user-friendly. This paper provides a comprehensive overview of the methodology%2C experiments%2C and results%2C highlighting the effectiveness of deep learning in medical image analysis.?quality=80&w=800)
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