Sickle Cell Disease (SCD) continues to be a major challenge to people's health worldwide, especially in areas that lack sufficient resources, where early and accurate diagnosis is of utmost importance for proper clinical management. Automated medical image analysis powered by deep learning has become a highly effective way to improve diagnostic precision and speed. This work is a comparative evaluation of three major convolutional neural network (CNN) architectures, AlexNet, MobileNetV2, and InceptionResNetV2, in the prediction of SCD from microscopic blood smear images. Accuracy, precision, recall, F1-score, and computational efficiency were the metrics used to evaluate the models.

The InceptionResNetV2 obtained the highest accuracy (94%) and also showed the greatest classification robustness; thus, it is very suitable for incorporation into advanced clinical diagnostic systems, according to experimental results. MobileNetV2, with slightly lower accuracy (93%), was highly computationally efficient and had a very short inference time, which makes it very suitable for real-time deployment in healthcare environments with limited resources. On the other hand, AlexNet, a CNN breakthrough, exhibited comparatively lower predictive performance.

Therefore, the findings of this study suggest that the best model choice will depend on the intended use scenario, as a trade-off between diagnostic accuracy and computational constraints. This work moves the field of AI-assisted hematology forward and is a deep learning breakthrough that has enormous potential in the early diagnosis and management of Sickle Cell Disease.