TumorInsight: GAN-Augmented Deep Learning for Precise Brain Tumor Detection
CLAHE; DCGAN; Deep Learning; Gamma Correction; MRI
In addition to the shortage in data as well as the low quality of MRI images, one of the most difficult tasks in contemporary medical imaging is the diagnosis of tumors in brain. This work presents a new approach to enhance diagnostic accuracy using sophisticated preprocessing techniques. Combining BRATS 2023 and Cheng et al. datasets to apply cutting-edge deep learning preprocessing methods with Generative Adversarial Networks (GANs), specifically DCGAN, Contrast Limited Adaptive Histogram Equalization (CLAHE), and gamma correction, it aims to significantly improve the quality of MRI images. As a result, updated data should be generated with greater precision and detail, making it possible to identify tumor-affected areas with greater accuracy. Thorough assessment, demonstrated by metrics such as Accuracy (0.98), Specificity (0.99), Sensitivity (0.99), AUC (0.65), Dice Coefficient (0.67), and Precision (0.71), highlights possible advancements in brain tumor identification and treatment, thereby highlighting the effectiveness of the suggested approach. 2024 IEEE.
5th International Conference on Circuits, Control, Communication and Computing, I4C 2024, pp. 100-105.
Institute of Electrical and Electronics Engineers Inc.
Shekhar R., School of Computing, Dit University, Dehradun, India; Rana M., School of Sciences, Christ University, Delhi NCR, India