SCDNet 1.0: Adaptive CNN Framework for Sickle Cell Disease Detection with OTSU Segmentation and Gaussian Filter

Abstract

Sickle Cell Disease (SCD) is a hereditary blood disorder characterized by abnormally shaped red blood cells, causing anemia, vascular blockages, and severe health complications. Early and accurate diagnosis is essential, but access to reliable diagnostic tools is often limited in resource-constrained settings. This study proposes an automated image segmentation and classification pipeline to improve SCD detection. Using the ErythrocytesIDB dataset, which contains labeled erythrocyte images classified as circular, elongated, or irregular, Gaussian Filter is applied for denoising, followed by OTSU thresholding for segmentation. Data augmentation techniques, including rotation, shifting, and flipping, enhance model robustness and generalization. A Convolutional Neural Network (CNN) integrated with an attention mechanism is developed for accurate erythrocyte classification, and five optimizers: ADAM, SGD, RMSPROP, ADAMAX, and NADAM are systematically evaluated. Validation via 5-fold cross-validation demonstrates that the proposed preprocessing and augmentation steps significantly improve performance. The CNN with attention optimized using NADAM achieves the highest test accuracy of 98.33%, outperforming baseline and recent state-of-the-art models. The proposed pipeline provides a reliable, efficient, and scalable solution for automated SCD detection, particularly suitable for regions with limited access to advanced medical diagnostics.