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Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 Detection by Islam, Md Shofiqul; Hasan, Khondokar Fida; Shajeeb, Hasibul Hossain; Rana, Humayan Kabir; Rahmand, Md Saifur; Hasan, Md Munirul; Azad, AKM; Abdullah, Ibrahim; Moni, Mohammad Ali is a scholarly article available to read on EtoBox.

What is Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 Detection about?

This study presents a comprehensive review of the potential of multimodal deep learning (DL) in medical diagnosis, using COVID-19 as a case example. Motivated by the success of artificial intelligence applications during the COVID-19 pandemic, this research aims to uncover the capabilities of DL in disease screening, prediction, and classification, and to derive insights that enhance the resilience, sustainability, and inclusiveness of science, technology, and innovation systems. Adopting a systematic approach, we investigate the fundamental methodologies, data sources, preprocessing steps, and challenges encountered in various studies and implementations. We explore the architecture of deep learning models, emphasising their data-specific structures and underlying algorithms. Subsequently, we compare different deep learning strategies utilised in COVID-19 analysis, evaluating them based on methodology, data, performance, and prerequisites for future research. By examining diverse data types and diagnostic modalities, this research contributes to scientific understanding and knowledge of the multimodal application of DL and its effectiveness in diagnosis. We have implemented and an

Author

Islam, Md Shofiqul; Hasan, Khondokar Fida; Shajeeb, Hasibul Hossain; Rana, Humayan Kabir; Rahmand, Md Saifur; Hasan, Md Munirul; Azad, AKM; Abdullah, Ibrahim; Moni, Mohammad Ali

Published

2025

Language

EN