Al-Kitab Journal for Pure Sciences

: The field of image processing has been revolutionized by Convolutional Neural Networks (CNNs), which exhibit exceptional capability in feature extraction and accurate image classification. However, training CNNs requires large volumes of annotated data and significant computational resources. Considering these challenges, transfer learning has emerged as a promising approach to reducing the dependence on labeled data and computational resources. Transfer learning involves utilizing knowledge gained from a source task to improve the training process for a target task. This technique has demonstrated considerable benefits; however, it also possesses certain limitations. Consequently, this survey explores the advantages and constraints of transfer learning and the various factors that influence its effectiveness in optimizing image processing using CNNs. Additionally, the survey investigates the most recent advancements and research in the field of transfer learning specifically for image processing with CNNs. In summary, this comprehensive analysis highlights the significance of transfer learning in the context of optimizing image processing with CNNs, providing unique insights into this rapidly evolving domain.


Introduction:
Convolutional Neural Networks (CNNs) have revolutionized the field of image processing with their outstanding ability to extract features and classify images. CNNs learn hierarchical representations of visual patterns through multiple layers of processing raw image data, making them highly efficient at identifying patterns and features. One of the most significant achievements of CNNs is their success in improving image resolution. Traditional methods like interpolation and bicubic upscaling have limitations and may result in blurry and distorted images. CNNs, on the other hand, employ super-resolution techniques to produce high-quality and sharp images with increased resolution [1] CNNs have also proven effective in image restoration tasks such as denoising. CNNs for denoising are capable of enhancing low-light images by eliminating noise patterns, resulting in more detailed and clearer images [2]. In the field of medical imaging, segmentation techniques based on CNNs have been utilized to identify and isolate specific organs or abnormalities in medical images. This application has proven helpful in diagnosing medical conditions and improving treatment planning [3]Transfer learning is a technique widely used in machine learning to improve model performance on related tasks [4] In this method, knowledge gained from a pre-existing model is used to enhance the effectiveness of a new task involving a smaller data set. Typically, a pre-existing model undergoes training on a large data set designed for a specific task, such as image classification It serves as an invaluable resource for researchers and professionals in the field, offering valuable insights into cutting-edge approaches to enhance the performance of CNN-based image processing applications.

2-How does Transfer Learning Work?
Transfer learning is a popular machine learning technique that aims to improve the performance of models on related tasks by leveraging knowledge gained from pre-trained models [7]. Typically, the pre-trained model undergoes training on an extensive dataset tailored to a particular task, like classifying images [8]. The knowledge gained from the pre-trained model's weights and biases is then transferred to a new task with a smaller dataset, improving the performance of the new model. This technique is particularly useful when there is a lack of sufficient training data for the new task or when training a new model from scratch is computationally expensive [9]. In the process of transfer learning, it is common practice to keep the lower layers of the pre-trained model unchanged and utilize them as fixed feature extractors.
Meanwhile, the higher layers are adjusted or fine-tuned to acquire features specific to the task at hand [10]. The pre-trained model has acquired generic features that hold significance across numerous tasks, including edge detection, texture recognition, and object representation. These features can be leveraged for a new task, leading to a reduction in the volume of training data needed and enhancing the performance of the new model, particularly when the new dataset is small and like the original dataset. This technique has been successfully applied to various fields, including computer vision, natural language processing, and speech recognition [7].The figure 1, [11] illustrates the use of a pre-trained model that was trained on a large image dataset (ImageNet) and fine-tuned on a new dataset with different classes and updated weights. In the context of image classification, Table 1 presents an overview of the top five CNN models, each offering pre-trained weights and biases that can be effectively employed for transfer learning. To determine the parameter, count for each filter, we employ the formula (a * b * c) + 1, where a * b represents the filter dimensions, c denotes the number of filters in the preceding layer, and the additional 1 account for the bias. The models are arranged chronologically, commencing with the initial-generation LeNet [12] and AlexNet [13], which were developed in 1998 and 2012, respectively. VGG16 [14] stands out as the pioneering deep model, while GoogleNet [15] introduced the innovative concept of blocks, and ResNet50 [16] introduced residual blocks featuring skip connections between layers. ResNet effectively resolves the vanishing gradient problem, ensuring adequate updates to the weights of earlier layers during training. Notably, all models employ the SoftMax function in the classifier head, except for LeNet-5, which utilizes the hyperbolic tangent function.

3-Literature Review
Transfer learning has found its application in a multitude of tasks related to image processing that employ CNNs, like image classification, object detection, and semantic segmentation. They also highlight the potential of this approach for improving accuracy and reducing the need for large amounts of training data.

4-Discussion
The survey report highlights the power of transfer learning in training CNNs for image processing tasks. CNNs have shown impressive results in image processing tasks such as image classification, super-resolution, denoising, and medical image segmentation. However, the training process for these models necessitates substantial quantities of annotated data and computational resources. Transfer learning addresses these challenges by reusing the knowledge learned from a pre-trained model for a source task and applying it to a new task with a smaller dataset. [14] The report also discusses the best practices and limitations of using transfer learning in different scenarios. One of the limitations of transfer learning is that the source task and the target task need to be like some extent. Otherwise, the performance gain from transfer learning may be limited. Another limitation is the risk of transferring irrelevant or harmful features from the source task to the target task, which may lead to overfitting or poor performance. [49] The report highlights that transfer learning is widely used in various fields, including computer vision, natural language processing, and speech recognition. As such, transfer learning is Anticipated to have a significant role a crucial role in advancing the fields of machine learning and artificial intelligence in the future. [50] The report also provides an overview of the latest developments in transfer learning techniques and their applications. The top five CNN models widely recognized for image classification, which come with pre-trained weights and biases that can be used for transfer learning, have been summarized in Table1.

5-Conclusion and Future Work
Transfer learning has emerged as a promising solution to reduce the dependency on labeled data and computing resources. The pre-trained model has acquired generic characteristics that are pertinent to various tasks, such as edge detection, texture recognition, and object representation. Utilizing these features for a new task allows for a reduction in the necessary training data and an enhancement in the performance of the new model, especially when the new dataset is small and bears resemblance to the original dataset. The survey report offers valuable perspectives on cutting-edge techniques to optimize the performance of image processing applications based on CNNs. The report also discusses the limitations and best practices for using transfer learning in different scenarios in the future, there is potential for further exploration of transfer learning methods in diverse domains and applications beyond its current scope, including natural language processing and speech recognition. Moreover, the applicability of transfer learning can be extended to other neural network architectures like recurrent neural networks and generative adversarial networks, to improve their performance on related tasks. Additionally, research can be conducted to optimize transfer learning techniques for specific types of datasets and tasks, as well as explore the potential of unsupervised transfer learning for improving model performance. [24] 6.