# Improving Medical Image Quality Using a Super-Resolution Technique with Attention Mechanism
## 1 Introduction  
Deep learning has emerged as a critical tool in image recognition and medical applications, including melanoma diagnosis. Melanoma is a highly lethal skin cancer, making early detection essential for improving patient outcomes [1]. However, accurate diagnosis remains challenging due to subtle lesion appearances and diverse clinical presentations. According to a pre-COVID-19 analysis report, as of 2015, 3.1 million people had the active disease, resulting in approximately 60,000 deaths. Therefore, dermatologists have developed extensive data and algorithms for diagnosing melanoma, primarily based on deep learning. Meanwhile, artificial intelligence is currently making innovative advancements through its complementary relationship with big data. The demand for data processing and understanding increases substantially as the volume of data increases. Consequently, artificial intelligence can provide better insights and predictive capabilities using big data. Big data and artificial intelligence can synergize and result in unpredictable changes andinnovations. Among these, super-resolution imaging using artificial intelligence is investigated. High-resolution images contain more detail than low-resolution ones. However, acquiring high-resolution images is expensive. Therefore, super-resolution techniques have been incorporated to reconstruct high-resolution images from low-resolution ones. These approaches have shown potential for addressing challenges in medical imaging, such as low spatial resolution and diagnostic limitations, but their application remains complex and demanding [2; 3; 4; 5]. However, super-resolution techniques are challenging [6; 7]. Generating high-resolution images is computationally complex, requires significant computational resources and time, and may not be suitable for real-time applications. However, in medical imaging, fine details such as small anatomical structures can convey essential information for diagnosis. For instance, applying artificial intelligence in the diagnosis, treatment, and prognosis assessment of pancreatic cancer has been discussed. Artificial intelligence has been used to aid in the imaging-based diagnosis of pancreatic cancer by automatically identifying and classifying pancreatic tumors based on imaging data from computed tomography and magnetic resonance imaging [8]. It can also predict drug treatment responses and propose personalized treatment plans. Models and algorithms that predict the prognosis of pancreatic cancer have been developed by integrating clinical information, genetic data, and histological tissue characteristics [9]. Therefore, artificial intelligence introduced through super-resolution techniques can impact diagnosis substantially [10].  
Therefore, deep learning and artificial intelligence technologies considerably assist in diagnosis and analysis across various medical disciplines through appropriate data training and parallel computing. This study aimed to apply super-resolution techniques to improve the accuracy and efficiency of melanoma diagnosis. The subsequent section discusses the proposed architecture.