## 3 Methodology
### Transfer Learning
#### 3.1.1 Models Based on Image Recognition  
Image classification is a fundamental task in computer vision, where the objective is to assign a specific label to an input image. CNNs have become the foundation of image recognition due to their unmatched ability to extract detailed features from images. They are composed of convolutional layers, pooling layers, and fully connected layers, arranged in a robust architecture to efficiently process and classify images. The convolutional layer extracts features from the input image by applying filters (kernels) through element-wise multiplication with local receptive fields of the image. These filters move across the image's width and height, generating a feature map that highlights important patterns. Afterward, a pooling layer reduces spatial dimensions of features, and fully connected layers perform the final classification. The architectural layout of CNN is shown in Figure 3.  
Training CNN models for image recognition typically requires large datasets, such as ImageNet, which contains millions of labelled images across a wide variety of object classes. The ImageNet dataset [48] comprises 1,281,167 training images, 50,000 validation images, and 100,000 test images, and serves as a benchmark for evaluating the performance of different models.  
Figure 2: Comparison of traditional machine learning (**a**) and TL (**b**).  
Over the years, CNN architectures have undergone significant advancements, each introducing unique innovations that have contributed to the field of image recognition:  
* AlexNet: Introduced in 2012, AlexNet [36] revolutionized the field by winning the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). The architecture consists of 8 layers, including 5 convolutional layers and 3 fully connected layers. A key feature of AlexNet was the use of overlapping pooling layers, which efficiently extracted spatial features (Figure 4a).
* SqueezeNet: Known for its efficient use of parameters, SqueezeNet [49] achieved accuracy comparable to AlexNet while drastically reducing model size. It employs a Fire module, which combines squeeze and expand layers, frequently using 1 \(\times\) 1 filters instead of 3 \(\times\) 3 filters. SqueezeNet demonstrated that lightweight CNN architectures could be effective without compromising accuracy (Figure 4b).
* GoogLeNet: Also known as Inception, GoogLeNet [50] won the ILSVRC 2014 competition with its 22-layer architecture. The Inception modules within GoogLeNet use parallel convolutional filters to capture spatial patterns at multiple scales, allowing the network to process data efficiently across varying resolutions (Figure 4c).
* Residual Network (ResNet): ResNet addressed the challenge of training deep networks by introducing skip connections, also known as residual blocks [51]. These blocks help maintain gradient flow during training, allowing for the successful training of very deep networks. ResNet-18 and ResNet-50 exemplify the power of these connections, with ResNet-18 using 8 iterations of a 2-layer residual block and ResNet-50 employing 16 iterations of a 3-layer bottleneck block (Figure 4d).  
Figure 4: Pre-trained network architectures: (**a**) AlexNet (**b**) SqueezeNet, (**c**) GoogLeNet, and (**d**) ResNet-18. In ResNet-50, each 2-layer residual block of ResNet-18 is replaced by a 3-layer bottleneck block.  
Figure 3: The basic principle of CNN.