Lung Tumor Classification Optimizer with Augment Input Images - Praveena, R.; Babu, T. R. Ganesh; Mahalakshmi, V.; Sangeetha, C.; Shobana, V. (Journal of Innovative Image Processing, 2024)
This work focuses on the accurate classification of lung tumors in Computed Tomography (CT) images to determine tumor type and stage, essential for guiding treatment decisions. In this research, pretrained model VGG19 and Generative Adversarial Networks (GANs) models are used to classify the lung tumor in order to enhance the classification accuracy by increasing the diversity of the training dataset. The dataset comprises 1097 CT image samples, with 120 classified as benign, 561 as malignant, and 416 as normal cases. The coding for this work was developed using Python, with TensorFlow as the deep learning platform, and simulations were conducted on Google Colab. Performance metrics such as accuracy, sensitivity, specificity, and F1 score are evaluated to assess the effectiveness of the classification model. The datasets were obtained from the Iraq-Oncology Teaching Hospital.
Keywords: GAN; Lungs Tumor; Augmentation; Benign; Malignant