# Transfer Learning-Based Classification of Maximly Sinus Using Generative Adversarial Networks
###### Abstract  
Paranasal sinus pathologies, particularly those affecting the maxillary sinuses, pose significant challenges in diagnosis and treatment due to the complex anatomical structures and diverse disease manifestations. The aim of this study is to investigate the use of deep learning techniques, particularly generative adversarial networks (GANs), in combination with convolutional neural networks (CNNs), for the classification of sinus pathologies in medical imaging data. The dataset is composed of images obtained through computed tomography (CT) scans, covering cases classified into \"Moderate\", \"Severe\", and \"Normal\" classes. The lightweight GAN is applied to augment a dataset by creating synthetic images, which are then used to train and test the ResNet-50 and ResNeXt-50 models. The model performance is optimized using random search to perform hyperparameter tuning, and the evaluation is conducted extensively for various metrics like accuracy, precision, recall, and the F1-score. The results demonstrate the effectiveness of the proposed approach in accurately classifying sinus pathologies, with the ResNeXt-50 model achieving superior performance with accuracy: 91.154, precision: 0.917, recall: 0.912, and F1-score: 0.913 compared to ResNet-50. This study highlights the potential of GAN-based data augmentation and deep learning techniques in enhancing the diagnosis of maxillary sinus diseases. m 2024 14, 3083. [[https://doi.org/10.3390/appl/14073083](https://doi.org/10.3390/appl/14073083)]([https://doi.org/10.3390/appl/14073083](https://doi.org/10.3390/appl/14073083))  
[PERSON] &. [PERSON] (c) 2024 1 2024  
complicated anatomical shapes and drainage channels. The frontal sinus, for example, empties into the middle meatus of the nasal cavity via the frontonasal duct [5]. The ethmoid sinuses are labyrinthine, composed of several tiny cells that drain into the middle and superior meatuses. The sphenoid sinus empties into the sphenoethmoidal recess [7]. The anatomical and functional distinctions between these sinuses lead to the distinct problems and pathologies identified in the maxillary sinus when compared to the other paranasal sinuses. Untreated sinusitis can cause serious problems such as infection spreading to surrounding tissues, the development of chronic illnesses, and a negative influence on general health [8]. Recognizing the severity of sinusitis in its early stages is critical for successful treatment and avoiding complications. The symptoms of sinusitis coincide with those of other common illnesses, such as seasonal influenza and colds, complicating the diagnosis even further. The closeness in symptoms frequently leads to a spike in referral requests for radiograph screening, putting a significant strain on healthcare resources [8]. However, even experienced radiologists have significant challenges when interpreting these CT images. The careful examination of various sinus regions necessitates a high degree of skill and time-consuming efforts, frequently resulting in diagnostic delays and the possible loss of vital information [9]. As the need for accurate and timely diagnoses increases, there is an urgent need to improve and streamline radiological workflow. Automating the examination of CT scans for sinus-related disorders becomes critical for addressing these problems, as it has the potential to increase diagnosis accuracy, decrease the strain on healthcare personnel, and speed up patient care. The major goal of this study is to address the difficulties involved with diagnosing and determining the severity of sinusitis, with a particular emphasis on the maxillary sinus. Deep learning models, notably convolutional neural networks (CNNs), have demonstrated promising outcomes in medical image interpretation, including sinus-related diseases [10]. However, the lack of balanced datasets makes it difficult to train accurate and stable models. This paper tackles this problem by using generative adversarial networks (GANs) to balance data samples [11].