Design of a Hybrid VGG 19- BN with a Deep Mask Region-based Convolutional Neural Network framework for Sickle Cell Anemia Detection and Classification

Abstract

Sickle Cell Anemia (SCA) is a genetic blood disorder where red blood cells change into a sickle shape, which hinders oxygen delivery and causes severe health issues. Prompt and accurate detection is crucial for the effective treatment and management of individuals with SCA. Existing diagnostic methods are often labour-intensive, manual, and subject to variation due to personal interpretations by healthcare professionals. Automated diagnostic systems face challenges such as inefficient feature extraction and classification, inconsistent blood smear quality, and a lack of available datasets. This paper proposes a hybrid deep learning framework based on VGG19 with Batch Normalization (VGG19-BN) with CNN to address these challenges in the identification and classification of SCA. The methodology includes pre-processing the images to enhance quality and standardize input data. BN layers are integrated into the VGG19 framework to stabilize training, reduce overfitting, and accelerate convergence. The convolutional layers extract features to classify RBC into normal and sickle categories. The framework was trained and validated using high-quality blood smear images. The primary objective of this study was to develop a reliable and efficient diagnostic tool capable of achieving high accuracy while remaining user-friendly and interpretable in clinical settings. The results showed that the VGG19-BN model outperformed baseline deep learning systems and traditional techniques, achieving 97.2% accuracy in classification, 96.1% sensitivity, and 98.1% precision. By improving both accuracy and efficiency, incorporating this method into clinical workflows could revolutionize SCA diagnosis and improve patient outcomes.