In recent years, deep learning techniques (Shamshirband et al., 2021) have transformed the landscape of high-dimensional signal processing and intelligent diagnostics. Given the rising demand for efficient and accurate systems in fields such as medical imaging, neuroinformatic (Richards et al., 2019), and various high-stakes diagnostic applications, this Research Topic delves into advances in non-linear processing methods that address the complexity of real-world signals. The unique contributions in this collection underscore how cutting-edge approaches in neural networks (Ke et al., 2022), particularly those tailored for high-dimensional and nonlinear data such as EEG, MRI and Perfusion-CT (Strambo et al., 2018), are reshaping diagnostic technologies and interpretability. The aim of this Research Topic is to gather significant studies that explore the intersection of deep learning, non-linear signal processing, and intelligent diagnostic applications. Each contribution (Ke et al., 2020). We anticipate that this collection will inspire 86 ongoing innovation and spur collaboration within the research community.