## 1 Introduction  
Medical imaging is crucial for accurate diagnosis, utilizing CT, MRI, PET, X-ray, and ultrasound to reveal internal structures [1, 2]. However, the noise inherent in these imaging techniques, such as Gaussian noise in CT and MRI, compromises image quality and challenges accurate diagnosis, particularly in detecting small lesions and tumors [3]. Traditional noise reduction methods like non-local means, wavelet transforms, and spatial filters often over-smooth images, obscuring critical details needed for tumor delineation and compromising diagnostic accuracy [4, 5, 6]. Recent advancements in deep learning have shown promise in medical image denoising [7]. Techniques like fractal networks (FNs) [8],variational autoencoders (VAEs) [9], and Generative Adversarial Networks (GANs) [10] have improved the visual quality of CT and MRI scans. Methods such as U-Net [11] architectures and convolutional neural networks (CNNs) [12] have effectively reduced noise and recovered degraded images. In another study [13], a denoising convolutional neural network (DnCNN) was utilized to recover MRI images degraded by Rician noise. In their work [14], they developed a sparse convolutional neural network (SCNN) along with a deep learning-based reconstruction model (DLRM) to denoise brain MR images and detect lesions. A convolutional autoencoder-decoder with residual learning (RED-CNN) [15] was successfully employed to estimate a normal-dose CT image from its low-dose counterpart. DenseNet [16] and block-matching 3D (BM3D) [17] have been used for Content-Noise Complementary Learning. While DenseNet faces high memory usage, limiting real-time noise removal, BM3D excels with Gaussian noise but struggles with structured noise and large images. A conditional GAN with a VGG (Visual Geometry Group) (CGAN-VGG) showed perceptual loss in the ability to preserve perceptual quality and fine details; that said, instability in training when balancing the adversarial and perceptual losses is challenging and often leads to overfitting [18]. The Wasserstein GAN was used in [19; 20] for low-dose denoising, and the performance indicated improved training instability by reducing sudden discriminator dominance. Despite their success, these approaches face challenges in preserving fine structural details and preventing over-smoothing [21], which are essential for detecting small tumors and maintaining diagnostic accuracy. They are faced with difficulty in detecting low-contrast noise [22], soft tissue differentiation, and low signal-to-noise ratio [23; 24]. In network architecture, GAN-based denoising methods are categorized into noise learning and content learning [25]. Noise learning effectively preserves image contrast and detail [26], while content learning provides stable noise suppression [27]. Combining these strategies has led to innovative models like Content-Noise Complementary Learning (CNCL), which enhances image quality by separating content from noise [28]. However, issues like mode collapse and over-smoothing remain prevalent in GANs, highlighting the need for balanced generator-discriminator dynamics to maintain image fidelity. Existing research has not explored tumor detection after image denoising; instead, it has concentrated on content and noise learning. To address these challenges, this study proposes the Adversarial Content-Noise Complementary Learning (ACNCL) model. Recent studies have explored dual-task frameworks that integrate denoising and diagnostic tasks for enhanced clinical interpretation. For example, [PERSON] [29] proposed a multi-task deep learning approach for simultaneous denoising and classification, achieving improved robustness to noisy data. Similarly, [PERSON] [30] demonstrated that content-aware GANs could significantly enhance lesion visibility in low-dose CT scans. These works validate the notion that denoising should not be an isolated pre-processing step but integrated with diagnostic purposes.