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What is Sickle Cell Classification Using Deep Learning about?

The paper presents a deep learning model utilizing ResNet50 for the classification of sickle cells in blood smear images, achieving a test accuracy of 93.88%. It highlights the challenges of manual identification of sickle cells and emphasizes the need for automated methods to improve accuracy and efficiency in diagnosis. The study discusses the methodology, including data preprocessing and the use of transfer learning to adapt a pre-trained model for the specific task of sickle cell classification.

Author

Shankar sarji.p

Language

EN