{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T15:15:06Z","timestamp":1776870906731,"version":"3.51.2"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:p>Medical image reconstruction and enhancement play a critical role in improving the reliability of computer assisted disease analysis. In this study, a simulation-based hybrid framework is proposed that integrates a deep neural network (DNN) with a cubature Kalman filter (CKF) to combine nonlinear state estimation with learning-based refinement for improved image reconstruction and organ-wise disease classification. The framework is evaluated in a controlled simulation environment using synthetic multimodal radiology-pathology images representing four organs (liver, kidney, lung, and heart) and three disease severity levels (normal, mild, and severe). The proposed hybrid approach consistently demonstrates higher reconstruction fidelity and classification performance than standalone CKF and DNN models. Quantitative evaluation using the peak signal-to-noise ratio and the structural similarity index measure indicates improved structural preservation and noise reduction. In addition, classification analysis using confusion matrices and derived performance metrics demonstrates reliable discrimination between disease severity levels. Across the simulated cases, improvements of approximately 5\u201310% in classification accuracy and higher reconstruction quality are achieved relative to baseline methods. These findings suggest that the integration of nonlinear filtering with deep learning can provide a robust computational framework for multimodal medical image reconstruction and organ-wise disease analysis. Although the results are based on simulation experiments, the proposed approach demonstrates potential for future validation using real clinical imaging datasets.<\/jats:p>","DOI":"10.3389\/fmed.2026.1770289","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T14:14:43Z","timestamp":1776867283000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multimodal medical image reconstruction and organ-wise disease classification using a hybrid deep learning-Kalman filtering framework"],"prefix":"10.3389","volume":"13","author":[{"given":"Saad","family":"Arif","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, College of Engineering, King Faisal University","place":["Al-Ahsa, Saudi Arabia"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"108200","DOI":"10.1016\/j.cmpb.2024.108200","article-title":"All you need is data preparation: a systematic review of image harmonization techniques in multi-center\/device studies for medical support systems","volume":"250","author":"Seoni","year":"2024","journal-title":"Comput Methods Prog Biomed"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"103671","DOI":"10.1016\/j.media.2025.103671","article-title":"Deepspv: a deep learning pipeline for 3D spleen volume estimation from 2D ultrasound images","volume":"105","author":"Yuan","year":"2025","journal-title":"Med Image Anal"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"109256","DOI":"10.1016\/j.compbiomed.2024.109256","article-title":"A systematic review of generalization research in medical image classification","volume":"183","author":"Matta","year":"2024","journal-title":"Comput Biol Med"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"110094","DOI":"10.1016\/j.compbiomed.2025.110094","article-title":"Generative adversarial networks in medical image reconstruction: a systematic literature review","volume":"191","author":"Hussain","year":"2025","journal-title":"Comput Biol Med"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1254","DOI":"10.1109\/TAC.2009.2019800","article-title":"Cubature kalman filters","volume":"54","author":"Arasaratnam","year":"2009","journal-title":"IEEE Trans Autom Control"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1109\/TMI.2023.3325703","article-title":"Zero-shot medical image translation via frequency-guided diffusion models","volume":"43","author":"Li","year":"2024","journal-title":"IEEE Trans Med Imaging"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1109\/TMI.2024.3483109","article-title":"MRI super-resolution with partial diffusion models","volume":"44","author":"Zhao","year":"2025","journal-title":"IEEE Trans Med Imaging"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.intonc.2025.06.007","article-title":"Deep learning in abdominal organ segmentation: a review","volume":"1","author":"Hu","year":"2025","journal-title":"Int. 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