2026-04-17T01:19:57Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4449082026-02-06T02:55:07Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Flores Arroba, Alexandra Macarena author Huilca Cabay, Víctor José author Palacios Arias, César Augusto author López Montero, María José author Delgado Brito, Omar Dario author Paredes Regalado, María Belén author 2025-05-01 Complex-valued neural networks have emerged as an effective instrument in image reconstruction, exhibiting significant advancements compared to conventional techniques. This study introduces an innovative methodology to tackle the difficulties related to image reconstruction within medical microwave imaging. Initially, in the estimation phase, the proposed methodology integrates the Born iterative method with quadratic programming. Subsequently, in the refinement stage, the study explores the application of complex-valued neural networks to enhance the quality of reconstructions. The research emphasizes distinct complex-valued neural network architectures, namely, CV-UNET, CV-CNN, CV-MLP, and their corresponding performances. CV-UNET stands out as the best architecture, surpassing conventional methods and the other complex-valued neural networks variants. The complex-valued neural network improves the fidelity of reconstructions and simplifies the procedure by obviating the need for multiple training steps, a common prerequisite in real-valued neural networks. Peer Reviewed Postprint (published version) Inverse scattering problem Microwave imaging Machine learning Deep learning Complex valued neural network Born iterative method Convolutional neural networks From iterative methods to neural networks: Complex-valued approaches in medical image reconstruction