  Malaria remains one of the most pressing public health concerns globally,
causing significant morbidity and mortality, especially in sub-Saharan Africa.
Rapid and accurate diagnosis is crucial for effective treatment and disease
management. Traditional diagnostic methods, such as microscopic examination of
blood smears, are labor-intensive and require significant expertise, which may
not be readily available in resource-limited settings. This project aims to
automate the detection of malaria-infected cells using a deep learning
approach. We employed a convolutional neural network (CNN) based on the
ResNet50 architecture, leveraging transfer learning to enhance performance. The
Malaria Cell Images Dataset from Kaggle, containing 27,558 images categorized
into infected and uninfected cells, was used for training and evaluation. Our
model demonstrated high accuracy, precision, and recall, indicating its
potential as a reliable tool for assisting in malaria diagnosis. Additionally,
a web application was developed using Streamlit to allow users to upload cell
images and receive predictions about malaria infection, making the technology
accessible and user-friendly. This paper provides a comprehensive overview of
the methodology, experiments, and results, highlighting the effectiveness of
deep learning in medical image analysis.
