Anemia Identification from Blood Smear Images Using Deep Learning: An XAI Approach

Anemia is a prevalent hematologic disorder characterized by a reduction in the number of red blood cells or a decrease in their oxygen-carrying capacity. Accurate and timely diagnosis of anemia is crucial for effective patient management and improved health outcomes. In this proposed work, we developed a CNN-based model to classify individuals as having symptoms of Iron-deficiency Anemia (IDA) or not, using blood smear images. The CNN model will be trained and fine-tuned using this dataset to maximize the classification accuracy. This helps healthcare professionals to understand the system's decisions and assist in the diagnosis and management of anemia. Despite a relatively limited number of data samples, our proposed work showed promising results by leveraging augmentation techniques. We achieved an accuracy of 0.95, with precision and recall values of 0.9 and 0.89, respectively. The area under the Receiver Operating Characteristic (ROC) curve is reported as 0.98.