• Mr.Aditya Shivade
• Ms.Akanksha Thaware
• Mr.Pushkar Dasgoankar
• Mr.Tejas Jambharunkar

— Sickle cell anemia (SCA) is a genetic blood disorder characterized by the abnormal shape of red blood cells, leading to various health complications. Automated detection and classification of sickle-shaped cells through microscopic imaging can aid in early diagnosis and treatment. This study explores the application of convolutional neural networks (CNNs) for the detection of sickle cell morphology from microscopic images. A dataset comprising labeled images of normal and sickle-shaped red blood cells is utilized to train and evaluate the CNN model. The CNN architecture is designed to learn discriminative features from the images and classify cells into normal or sickle-shaped categories. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, demonstrate the efficacy of the proposed CNN-based approach for accurate identification of sickle cell anemia.

- Sickle cell anemia, Convolutional Neural Network, CNN, Image classification, Microscopic imaging, Red blood cells, Machine learning, Medical diagnosis, Automated detection, Feature learning, Performance evaluation

"SICKLE CELL DETECTION USING CNN FROM MICROSCOPIC BLOOD IMAGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.a291-a294, March-2024, Available :http://www.jetir.org/papers/JETIR2403038.pdf

"SICKLE CELL DETECTION USING CNN FROM MICROSCOPIC BLOOD IMAGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppa291-a294, March-2024, Available at : http://www.jetir.org/papers/JETIR2403038.pdf