###### Abstract  
The rising awareness of nutritional values has led to an increase in the popularity of meat-based diets. Hence, to ensure public health, industries and consumers are focusing more on the quality and freshness of this food. Authentic meat quality assessment methods can indeed be exorbitant and catastrophic. Furthermore, it is subjective and reliant on the knowledge of specialists. Fully automated computer-aided diagnosis systems are required to eradicate the variability among experts. However, evaluating the quality of meat automatically is challenging. Deep convolutional neural networks have shown a tremendous improvement in meat quality assessment. This research intends to utilize an ensemble framework of shallow convolutional neural networks for assessing the quality and freshness of the meat. Two compact CNN architectures (ConvNet-18 and ConvNet-24) are developed, and the efficacy of the models are evaluated using two publicly available databases. Experimental findings reveal that the ConvNet-18 outperforms other state-of-the models in classifying fresh and spoiled meat with an overall accuracy of 99.4%, whereas ConvNet-24 shows a better outcome in categorizing the meat based on its freshness. This model yields an accuracy of 96.6%, which is much better compared with standard models. Furthermore, the suggested models effectively detect the quality and freshness of the meat with less complexity than the existing state-of-the art techniques. convolutional neural networks; cross-domain transfer learning; ensemble learning; meat quality; performance metrics +
Footnote †: journal: Medical  
A significant amount of health benefits from meat consumption depend on its quality. Meat quality is greatly impacted by both external and internal factors [4]. Once exposed to light, dust, and microorganisms during transportation and sales, they take on an unappetizing appearance. As meat spoils quickly, it is critical to maintain its high quality in the face of this rising demand [5]. Thus, quality is becoming increasingly important in consumers' purchasing decisions as meat consumption rises [2]. The quality of meat is directly related to the survival and development of human beings [6]. Some consumers are even willing to pay a higher price to guarantee the quality of meat [7]. However, in recent years, many unscrupulous merchants have shown the tendency to take risks to seize the market and obtain huge profits, resulting in various quality problems. To uphold consumers' lawful rights and interests, government agencies and the food industry ought to pay special concern to the safety and nutritional value of meat [6]. Therefore, how to test and estimate the quality of meat has become the top priority of research. Evaluating various meat attributes to determine whether the meat is suitable for fresh consumption and preservation for a given period of time is referred to as meat quality assessment [8]. Meat has physical, biological, and chemical properties in general. Color, texture, pH, tenderness, and freshness are all meat characteristics for accessing quality. Among these factors, freshness is considered important in determining meat quality and safety [9]. This is due to the fact that consumers frequently use the external appearance of fresh meat as the primary criteria for choosing high-quality products for consumption [10]. Meat products, in general, are deemed fresh if fewer days have elapsed and stale if more days have elapsed, regardless of their actual freshness. The color of meat conveys an intuitive impression of freshness and ingredient composition. It is not only a comprehensive indicator reflecting differences, but it is also an important factor that changes as fresh meat ages. The other important quality factor is tenderness. Freshness, however, is a dynamic factor that is influenced by the processing and display conditions of the product. There are two major techniques to evaluate meat quality: subjective and objective [11].