A Computer Vision Based Food Recognition Approach for Controlling Inflammation to Enhance Quality of Life of Psoriasis Patients

Abstract

Deep learning becomes the spotlight in computer vision based recognition approaches in recent years. Psoriasis affects people of all ages around the world and causes inflammation on the skin with significant systemic disability and illness. Inflammatory foods increase inflammation rapidly, patients can easily control inflammation to enhance the quality of life by eliminating these foods from their everyday diet. This paper addresses a rapid food recognition approach to assist psoriasis patients to recognize fifteen highly inflammatory foods. Using image augmentation techniques, a dataset of 41250 images of different inflammatory foods have generated from 10000 images. AlexNet, VGG16, and EfficientNet-B0 have used in this study using the transfer learning approach, and EfficientNet-B0 has achieved the highest accuracy of 98.63% under the test set of 5250 images. AlexNet and VGG16 have achieved 87.22% and 93.79% accuracy, respectively. EfficientNet-B0 has consumed the lowest time in recognizing unseen images compared to others.