Non-invasive detection of anemia using lip mucosa images transfer learning convolutional neural networks - Mahmud, Shekhar; Mansour, Mohammed; Donmez, Turker Berk; Kutlu, Mustafa; Freeman, Chris (Frontiers in Big Data, 2023)
Anemia is defined as a drop in the number of erythrocytes or a drop in hemoglobin concentration below normal levels in healthy people. The bleaching value of the skin might vary based on the color of the skin, despite the fact that there is no quantifiable measurement. The pallor of the skin is best visible in locations where the cuticle is thin, such as the interior of the mouth, lips, or conjunctiva. This work focuses on anemia-related pallors and their relationship to blood count values and artificial intelligence as well. In this study, a deep learning approach using transfer learning and Convolutional Neural Networks (CNN) was implemented in which VGG16, Xception, MobileNet, ResNet50 architectures, were pre-trained to predict anemia using lip mucous images.A total of 138 volunteers (100 women and 38 men) participated in the work to develop the dataset which contain two image classes; healthy and anemia. Image processing was first performed on a single frame with only the mouth area visible, data argumentation was preformed and then CNN models were applied to classify the dataset lip images. Statistical metrics were employed to discriminate the performance of the models in terms of Accuracy, Precision, Recal and F1 Score.Among the CNN algorithms used, Xception was found to categorize the lip images with 99.28% accuracy, providing the best results. The other CNN architectures had accuracies of 96.38 % for MobileNet, 95.65 % for ResNet % and 92.39 % for VGG16. Our findings show that anemia may be diagnosed using deep learning approaches from a single lip image. This data set will be enhanced in the future to allow for real-time classification.