Image-based discrimination of ultrasound-assisted frozen meat using deep learning

Hamed Sardari, Mahmoud Soltani Firouz, Soleiman Hosseinpour, Pouya Bohlol,
Image-based discrimination of ultrasound-assisted frozen meat using deep learning,
Future Foods,
Volume 12,
2025,
100822,
ISSN 2666-8335,
https://doi.org/10.1016/j.fufo.2025.100822.
(https://www.sciencedirect.com/science/article/pii/S2666833525002813)
Abstract: The objective of this research is to investigate the possible applications of deep learning algorithms in categorizing ultrasound-assisted frozen meats according to their quality attributes, after thawing and cooking. The findings could offer significant insights into the utilization of deep learning algorithms for analyzing meat quality data and their potential in creating intelligent meat processing systems. Through training a convolutional neural network (CNN) model on a dataset of images categorized by ultrasound power levels, the model can identify and comprehend patterns and features related to specific power levels. The model achieves an accuracy rate of 97.75 % for identifying thawed ultrasonicated meats and 97.68 % for cooked ones. The model's impressive performance in classifying the images highlights its capability to accurately distinguish subtle variations in image quality and clarity.
Keywords: Deep learning; Ultrasonication; Meat freezing; InceptionV3; Convolutional neural network