## 3 Implementation Based on Generative Adversarial Network (GAN)  
The ACNCL denoising and tumor detection model leverages Generative Adversarial Networks (GANs) to separate content and noise in medical images, enhancing denoising and tumor detection. GANs are increasingly preferred over traditional CT and MRI scan methods due to their ability to model complex noise distributions. Unlike conventional techniques, GANs learn to differentiate noise from essential structures, preserving critical details like tumor boundaries. This results in sharper images optimized for tumor detection. Compared to Convolutional Neural Networks (CNNs), generative adversarial networks (GANs) offer a key advantage in generating high-quality images through adversarial training. While CNNs excel at feature extraction and classification, they typically require separate pre-processing for denoising before tumor detection. In contrast, GANs integrate noise removal and feature enhancement into a single process, improving generalization across diverse datasets. Despite their advantages, GANs face challenges such as mode collapse, where the generator produces limited variations and misses diverse tumor features. Additionally, over-smoothing may blur fine details, and training instability makes balancing the generator and discriminator difficult. To address these issues, adversarial training ensures that the discriminator learns to distinguish clean from noisy images, improving  
Figure 2: Flowchart representation of the proposed ACNCL model for image denoising and tumor detection in low-quality medical images. the generator's ability to retain tumor details while denoising effectively. To mitigate mode collapse, loss functions are adapted to penalize repetitive outputs, encouraging the generator to explore diverse image regions. This is essential in tumor detection, where variations in tumor shapes and structures exist. Training stability is enhanced through ACNCL's dual-task learning approach: one part of the network focuses on denoising, while another addresses tumor detection. This division provides clear denoising while preserving essential structures. By training the generator alongside a discriminator that distinguishes clean and noisy images, ACNCL generates realistic, high-quality outputs without overturning to specific noise patterns, ensuring robust tumor detection and improved medical image clarity. 1. **The GAN architecture**: It is structured with two primary components: a Generator and a Discriminator. The Generator uses multiple predictors, such as DrCNN, U-Net, and DenseNet, to process the degraded image, separating it into predicted content and predicted noise. The Generator also incorporates wavelet transformations (DWT) and a Context-Aware Anisotropic Gaussian Filter (CA-AGF) to enhance the accuracy of the predictions. The Discriminator Patch GAN architecture is employed, which is responsible for distinguishing between real and generated image pairs (i.e., real content vs. predicted content). The Discriminator also evaluates the tumor detection accuracy by incorporating specialized loss functions that penalize incorrect predictions of tumor regions. Figure 3 shows a GAN-based implementation of the proposed ACNCL model. The diagram is organized into several interconnected components:  
Input Degraded Image _(I_degraded):_ The degraded medical image, potentially containing a tumor, is passed through the Noise Predictor and Content Predictor sub-networks within the Generator. Noise Predictor (GAN-based): The noise predictor leverages models like U-net and DenseNet to estimate the noise components in the degraded image. It incorporates Discrete Wavelet Transform (DWT) and Context-Aware Anisotropic Gaussian Filter (CA-AGF) extraction to capture and enhance the noisy regions. The output is the Predicted Noise _(I_noise)_. **Content Predictor (GAN-based):** Simultaneously, the degraded image is processed through the content prediction pathway.