Work

Progressive semi-supervised learning for multimodal pneumonia diagnosis

Year: 2026

Type: article

Abstract: To develop a multimodal machine learning framework for pneumonia detection from lung sound recordings to address the challenge of timely and affordable diagnosis in resource-limited settings. We designed a progressive semi-supervised learning model that processes lung sound signals in three forms: time-series data, spectrogram representations, and wavelet transforms. Contrastive and diversity losses were introduced during progressive training to improve generalization and reduce overfitting with limited labeled data. The proposed framework achieved state-of-the-art performance with an accuracy of 97.85% and an F1-score of 97.80%, outperforming existing unimodal and multimodal benchmarks. This approach shows strong potential as a reliable and efficient noninvasive screening tool for pneumonia, offering robust performance with a minimal computational footprint.

Source: Biomedical Signal Processing and Control

DOI: 10.1016/j.bspc.2026.109750

Authors: Md Jobayer, Md. Mehedi Hasan Shawon, Mirza Rasheduzzaman, Mohammad Walidur Rahman, A.B.M.S.U. Doulah

Institutions: BRAC University, Bangladesh Medical University, Southeast University

Language: English

Topic: Phonocardiography and Auscultation Techniques

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $2,590