Impact of Wavelet Pre-processing on Deep Learning Architectures for Renal Cancer Diagnosis - Meireles Pereira Santos, Vinícius; Patrocinio, Ana Claudia; de Sousa, Pedro Moises (Revista de Informática Teórica e Aplicada, 2026)
Renal cancer is a lethal neoplasm where early detection is critical yet challenging. To define an optimal diagnostic pipeline, this study conducts a comparative analysis on a public tomography dataset (CT KIDNEY DATASET), evaluating the impact of a wavelet filter. Six deep learning architectures were tested, spanning classic (VGG16), modern (ResNet50), efficient (MobileNetV2), and attention-based (Vision Transformer - ViT) paradigms. Our results reveal a complex, architecture-dependent impact: wavelet pre-processing enables a ResNet50 to achieve a test accuracy of 100.00%. However, we demonstrate that a more efficient architecture, MobileNetV2, reaches the same state-of-the-art performance without this pre-processing step. These findings highlight that the optimal diagnostic pipeline is a balance between architectural efficiency and pre-processing complexity, positioning MobileNetV2 as the most pragmatic and effective solution.