Sickel cell anemia detection using constitutional neural network
International Research Journal of Modernization in Engineering Technology and Science · 2023
Sickle Cell Anemia (SCA) is a prevalent genetic blood disorder characterized by abnormal hemoglobin causing red blood cells to take one distinct sickle shape.Early and accurate detection of SCA is essential for effective treatment and management.This study present innovative approach to SCA detection utilizing Convolutional Neural Networks (CNNs), a class of deep learning algorithms known for their effectiveness in image analysis tasks.The proposed method involves the use of microscopic images of blood smears from patients.These images are preprocessed to enhance contrast, normalize intensities, and remove noise.Subsequently, CNN architectures are employed to automatically extract hierarchical features from the blood smear images.The trained CNN model learns discriminative features directly from the images, effectively capturing subtle differences between normal and sickled red blood cells (RBCs).