Transfer Learning Approach to Identify Food Allergy
Keywords:
Transfer Learning, Food Allergies, Adam, Resnet-50, Deep Learning ModelAbstract
Obesity, a curable medical illness caused by excessive calorie consumption, may lead to many health complications such as diabetes, high cholesterol, heart attacks, high blood pressure, and colon and prostate cancer. Computer-based solutions are routinely employed to solve these challenges. The goal of this project is to provide a system for detecting and diagnosing food allergies based on food images. The approach employs transfer learning (ResNET 50) to identify food kinds, validate labels in the Food 101 dataset, and deliver nutrients. The major objective is to establish a single framework capable of handling food allergy detection, location, and classification. The study additionally enhances weight parameter optimization using Adam and RMS Prop optimizers. Resnet-50 has the highest mean average accuracy of any transfer learning meta-architecture when using an Adam optimizer, at 95%. Based on another dataset, the suggested technique detects and offers nutrients for all types of food. Successful food allergy detection might lessen the harmful effects of diet management issues.
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