# Using Compressed JPEG and JPEG2000 Medical Images in Deep Learning: A Review
## 3 DL Model Performance on Compressed Medical Images  
_DL in Medical Imaging_  
Deep Neural Networks (DNNs) have substantially advanced medical image processing, enhancing diagnostic accuracy, treatment planning, and patient care. Earlier image analysis techniques relied on manually selected features and expert knowledge, which were often time-intensive and susceptible to human error. Notable milestones include optical scanning for mammogram abnormalities in 1967 [27], automated detection of suspicious lesions in breast radiographs in 1977 [28], and computer-assisted mammogram inspection in 1979 [29]. The emergence of ML aimed to address these challenges by automating feature selection and improving operational efficiency. Traditional ML models, such as Support Vector Machines (SVMs), decision trees, and random forests, require extensive feature engineering to identify relevant inputs [30], and have been applied successfully to tasks like image segmentation, object detection, and disease classification. Techniques such as Fourier, Cosine, Wavelet, and Gabor transforms, along with dimensionality reduction methods like Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), were essential for enhancing model performance [31; 32]. In contrast, DL models autonomously learn and extract hierarchical features from raw data, thus eliminating the need for manual feature engineering. This capacity has positioned DL as a transformative approach in medical imaging, with applications in Computer-Aided Diagnosis (CAD), lesion detection, organ and tumor segmentation, image enhancement, and patient prognosis [31; 33]. Figure 2 illustrates the primary distinctions between ML and DL in diagnostic binary classification tasks. \begin{table}
\begin{tabular}{c c c} \hline
**Artifacts** & **Implications** & **Compression Algorithms** \\ \hline Wavelet Artifacts: JPEG2000 & & \\ uses wavelet transform, which & These artifacts can obscure small & \\ can cause artifacts that appear & but clinically significant details, & \\ as ripple-like patterns or & such as small vessels or subtle & \\ smudges, particularly at higher & texture differences [23; 24; 25]. & \\ compression rates. & & \\ \hline Blurring: Loss of high-frequency & Blurring can obscure important & \\ detail, leading to a reduction in & diagnostic features, such as the & \\ image sharpness. & edges of tumors or the fine & \\  & structures of tissues, potentially & \\  & leading to misdiagnosis [24; 25; 26]. & \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 2: ML versus DL processes for classification problem (high-level). DL architectures for medical imaging are generally categorized as supervised or unsupervised techniques. In supervised methods, input data include labels, enabling models to learn to predict these labels on unseen data. By contrast, unsupervised techniques identify patterns without the need for labeled data. Convolutional Neural Networks (CNNs) are the most widely employed supervised DL architectures in medical imaging [34]. Known for their strength in image recognition and feature learning, CNNs maintain local spatial relationships within images while also reducing dimensionality efficiently [35]. In medical imaging, classification involves identifying objects or features within an image, such as determining the presence of a specific pathology. Segmentation divides an image into Regions of Interest (RoIs), such as separating tissues or lesion boundaries, while detection identifies and marks specific objects, like liver tumors in CT scans. CNNs are capable of processing both 2D images and 3D volumes like CT or MRI scans [35]. Figure 3 summarizes task types and applications using CNN architectures. Less common types of supervised DL architectures used in medical imaging are Recurrent Neural Networks, which are applied for processing sequences of images or regions, or for generating image captions [35; 36].