GenENet 1.0: A Deep Network Pipeline with Generative Adversarial Networks for Tumor Identification

Brain tumors must be diagnosed early and accurately in order to be treated effectively and with timely intervention. Despite the significant potential of deep learning in medical image analysis, its performance can be impeded by factors such as class imbalance and lack of data availability. In response to these challenges, we introduce GenENet 1.0, a deep learning pipeline that mixes InceptionV3 with GANs to enhance data quality and enable strong tumor classification. There is a significant reduction in data imbalance and an improvement in model generalization when GANs are utilized to generate a variety of synthetic samples. InceptionV3, renowned for its exceptional feature extraction, enhances the accuracy of categorization. GenENet 1.0 showed remarkable sensitivity and specificity of 97.50% when evaluated on a benchmark dataset of brain tumors. In addition, our model outperforms the state-of-the-art methods according to ROC analysis, which yielded an AUC-ROC score of 97.35%. The combination of GAN-based data augmentation and the modified InceptionV3 architecture enhances tumor detection, lowers misclassification, and increases diagnostic reliability. These results demonstrate that GenENet 1.0 is a powerful platform for identifying brain cancers and illustrate that automated medical image processing has come a long way. We endeavour to expand the dataset, refine the model, and integrate multi-modal medical imaging approaches in order to enhance real-world clinical application in the future.