## 4 Results and Discussions
### Comparison with Pre-Trained Models  
Several pre-trained models such as (Alexnet, Mobilenet-V2, Resnet-50, VGG-16, VGG-19, Densenet-201, and Efficientnet-b0) are implemented in this work. These models are customized for binary and three-class classification tasks. Faster and easier training is critical. To accomplish this, transfer learning is employed. For all pre-trained models, the initial layers and weights are transferred. Modification is performed in the final layers. Stochastic gradient descent with momentum (SGDM) optimizer is used to update the learnable parameters. An initial learning rate of \(1\times 10^{-4}\) and a batch size of 32 are selected. A brief description of pre-trained models follows:  
Alexnet: It is regarded as the first deep CNN architecture capable of producing better outcomes for image classification tasks. The inclusion of deeper layers and various parameter optimization strategies have significantly improved the architecture's learning capacity [31]. The major downside of the deeper network is overfitting. This issue is tackled by arbitrarily skipping some transformational units during the training phase. VGG-16 and VGG-19: Two standard deeper neural networks (VGG-16 and VGG-19) [32] are developed by the Visual Geometry Group (VGG) from Oxford University to explore the influence of network depth on classification accuracy. With a kernel size of \(3\times 3\), image features are extracted for better classification. Resnet-50: The performance of deeper networks may become saturated or even degraded due to the vanishing gradient problem. Residual networks are developed primarily to tackle the degradation issue encountered in deeper networks [33]. This is accomplished by incorporating residual connections (previous layer outputs connected to new layer outputs) into the network. Mobilenet-v2: The cornerstone of this architecture is an inverted residual block, which comprises three layers [34]. The expansion layer uses \(1\times 1\) kernel to expand the number of channels with an appropriate expansion factor. The second layer performs the depth-wise convolution using \(3\times 3\) kernels. Finally, the spatially filtered feature map is projected to low-dimensional space using a projection layer (\(1\times 1\) convolution). Furthermore, a residual connection is added to aid the gradient flow during backpropagation. Densenet-201: It comprises four dense blocks and three transition blocks [35]. Each dense block has varying number of dense layers (6, 12, 48, 32 in blocks 1, 2, 3, 4, respectively) which are densely connected. The network can be thinner and compact, as each layer receives feature maps from all proceeding layers. Efficientnet-b0: Efficientnet architecture incorporates the compound scaling method to uniformly scale network width, depth, and resolution [36]. It uses inverted bottleneck convolution, which consists of a layer that first expands the network and then compresses the channels. Instead of using the ReLU activation function, this model uses a new activation function, swish activation. Compared to the various models (b0-b7), Efficientnet-b0 requires fewer parameters. Table 5 describes the metrics obtained by various pre-trained models for a binary classification task. Experimental findings reveal that majority of the pre-trained models perform better in classifying fresh and spoiled meat images. Though Alexnet yields an accuracy of 98.8%, it can be trained faster compared to other models. Densenet-201 takes  
a longer training time compared to other models. The highest classification accuracy is obtained by the Resnet-50 and VGG-19 models. Table 6 represents the metrics obtained by various pre-trained models for multi-class classification. Experimental findings reveal that VGG-19 outperforms the other models in classifying the meat images into three different classes. This model correctly predicts 172 fresh meat images. Among 159 half-fresh images, 154 are correctly classified. Also, this model predicts 112 of 114 spoiled images correctly.