# Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review
## 3 DL Model Performance on Compressed Medical Images
Generative Adversarial Networks (GANs) are an example of unsupervised learning and are used to generate new data samples (synthesizing realistic medical images) that may be used to train other models [37]. In this work, based on the literature, we analyze how irreversible compression affects classification and segmentation tasks when using CNN architectures for medical images. Modeled after the architecture of the primary visual cortex [38; 39; 40; 41], CNNs are optimized for extracting and learning critical image features [42; 43]. Their multi-layered architecture abstracts raw pixel data through layers, including convolutional layers with Rectified Linear Unit (ReLU) activation and pooling layers. Convolutional layers utilize filters to detect localized features (edges, corners, textures), yielding activation maps that reveal the spatial distribution of these features. ReLU activation functions convert linear input combinations into non-linear outputs, capturing complex relationships and mitigating vanishing gradients, which stabilizes training [44]. Pooling layers reduce spatial dimensions, preserving key information and enhancing feature invariance, which aids in reducing dimensionality and overfitting. Fully connected layers then aggregate localized features into global representations, enabling complex tasks like classification [45]. Figure 4 outlines a basic CNN architecture. In medical imaging, CNN-based architectures like AlexNet [46], VGGNet [47], and ResNet [48] are widely used. However, DL models require large datasets; to address this, transfer learning [49] enables adaptation of pre-trained models on smaller datasets, such as MRI brain tumor segmentation [50]. CNNs are also integral to architectures like U-Net, which employs an encoder-decoder structure for tasks, such as lung CT image segmentation [51; 52]. Figure 3: Types of tasks and applications based on CNNs in medical imaging. One of the earliest applications of CNNs was lung nodule detection in chest X-rays [53]. CNNs are expected to optimize routine tasks for medical professionals, potentially enhancing their practice [54]. In some instances, DNN models can surpass radiologists' performance; for example, the study \"Radiologists versus Deep Convolutional Neural Networks: A Comparative Study for Diagnosing COVID-19\" found that DNNs outperformed radiologists in classifying COVID-19 images, achieving higher accuracy, sensitivity, precision, and F1-score [55]. However, other research indicates that larger datasets are necessary for DNNs to fully match radiologists' performance [56]. This work presents only a high-level overview of selected DL methodologies. However, extensive research has been conducted on the development and modification of complex DL architectures to tackle specific tasks in medical image analysis. A more comprehensive review of DL applications in this field, particularly concerning CNN architectures, can be found in numerous studies [57, 58, 59, 60, 32, 34, 35, 36, 37, 38, 39, 45, 54, 55, 56, 57, 58, 59, 61, 62, 63, 64, 65, 66].