## 3 Implementation Based on Generative Adversarial Network (GAN)
### Experimental Setup  
In this study, multiple deep learning architectures and signal processing techniques were integrated into a Generative Adversarial Network (GAN) framework for medical image denoising and tumor detection. The ACNCL model employs dual predictors: one for content preservation and another for noise estimation, allowing a complementary learning strategy. CNN-based architectures such as U-Net, DenseNet, and DnCNN are optimized using modified parameters such as replacing deconvolution with sub-pixel convolutions and applying Atrous Spatial Pyramid Pooling (ASPP) to enhance multi-scale feature extraction. Pre-processing involved normalization and resizing of image slices from CT and MRI datasets, which were then augmented using rotation and flipping to improve model robustness. (i) Predictors: To demonstrate the effectiveness of the proposed ACNCL model, we explored various predictors within our GAN-based model. Table 1 gives a summary of the predictors' contribution, including DnCNN, CA-AGF, DWT, U-Net, and DenseNet. (ii) Dataset Description  
(a) CT Dataset: The proposed denoising model was validated on a publicly available CT dataset collected from The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) specifically curated for evaluating medical image denoising techniques [32]. This dataset can be accessed at this link: [[https://www.cancerimagingarchive.net/collection/tcga-luad/](https://www.cancerimagingarchive.net/collection/tcga-luad/)]([https://www.cancerimagingarchive.net/collection/tcga-luad/](https://www.cancerimagingarchive.net/collection/tcga-luad/)), accessed on 5 May 2020. The dataset comprised 10 CT scans from anonymous patients, each containing 2D slices of abdominal regions and corresponding simulated 25% dose CT 2D slices. The full-dose data were acquired at 120 k and 220 effective mAs, while the 25% dose data were generated by adding Poisson noise into the projection data. The dataset included 2428 2D slice pairs (512 \(\times\) 512 pixels). We selected 1830 slice pairs from 7 patient scans for training and 598 CT slice pairs from 3 patients for testing. Another set of brain datasets for CT images was used to compare the performance of the results obtained. The dataset had dimensions of 512 \(\times\) 512 pixels and is a collection of human brains with neurological conditions, including cancer, tumors, and aneurysms. The dataset can be accessed at [[https://www.kaggle.com/datasets/trainingdatapro/computed-tomography-ct-of-the-brain/data](https://www.kaggle.com/datasets/trainingdatapro/computed-tomography-ct-of-the-brain/data)]([https://www.kaggle.com/datasets/trainingdatapro/computed-tomography-ct-of-the-brain/data](https://www.kaggle.com/datasets/trainingdatapro/computed-tomography-ct-of-the-brain/data)), accessed on 13 December 2024. 2. MRI: The dataset used in this experiment is a curated combination of three sources: the Figshare dataset, the SARTAJ dataset, and the Br35H dataset. The Figshare dataset provided a significant portion of the images, particularly for the glioma class, which was critical due to issues identified in the SARTAJ dataset. The SARTAJ dataset initially included images for various tumor classes, but it was noted that the glioma class images were not accurately categorized. To ensure the integrity of the experiment, these images were replaced with more reliable ones from the Figshare dataset. Finally, the Br35H dataset supplied the no-tumor class images, which were necessary for the negative class in the classification tasks. The dataset can be accessed at this link: [[https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset)]([https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset](https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset)), accessed on 4 January 2021. It contained abdomen 2D slices of 10 patients in a clinical trial containing 1311 2D slice pairs in total (544 \(\times\) 394 pixels).