GCNN-based Combined Denoising and Classification for Improved MRI Brain Tumor Identification

Medical image analysis is fundamental in modern healthcare, providing vital insights for early diagnosis and effective treatment planning. In the realm of neuroimaging, Magnetic Resonance Imaging (MRI) serves as a main resource for brain tumor detection. However, the clinical utility of MRI is intrinsically linked to image quality, wherein enhancing the noisy MRI scans remain as a significant challenge. This study introduces a novel GCNN approach for improving MRI image quality in brain tumor detection by jointly addressing the denoising and classification challenges. This study efficiently remove noise from MRI images by using the Denoising Convolutional Neural Network (DnCNN). A Convolutional Neural Network discriminator evaluates the quality of denoised images. Following denoising, a CNN-based classifier detects brain tumors in the denoised images. Empirical validation across diverse datasets demonstrates the proposed approach's efficacy in enhancing image quality and achieving robust tumor classification. GCNN presents a promising solution for advancing brain tumor diagnosis through integrated GAN-based image enhancement and classification. A cutting-edge technique for medical image analysis is demonstrated by GCNN.