Advancements in Deep Learning for Early Detection and Diagnosis Across Multiple Disease Domains

Identification and diagnosis of diseases in its early stages is one of the biggest determinants of improving patient health and needs, as well as decreasing general health care costs. Hence, with the application of deep learning, there has been a total change in the methods of medical diagnosis. This scoping review provides an employment of the current research progress of deep learning in different disease types, such as cancer, neurodegenerative diseases, and ophthalmologic diseases. This paper intends to compare different deep learning architectures, preprocessing methodologies used for data, and how data is incorporated into the framework, with a view of giving a glimpse of what researchers are currently doing in this fast-growing field, difficulties that are being encountered, as well as the future direction of the research domain being considered in this paper.

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Publication Details

Published
2024-11-13
DOI
https://doi.org/10.1109/ictacs62700.2024.10840864
Citations
12
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
8.49
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article

Advancements in Deep Learning for Early Detection and Diagnosis Across Multiple Disease Domains

Vishal Jain, Ibrohimbek Yusupov, Raj Kumar, Danish Ather et al.
12 citations
Artificial Intelligence in Healthcare
8.49
article

Advancements in Deep Learning for Early Detection and Diagnosis Across Multiple Disease Domains

Vishal Jain, Ibrohimbek Yusupov, Raj Kumar, Danish Ather, Sonia Duggal, Pinki Sagar
article en
12 citations

Abstract

Identification and diagnosis of diseases in its early stages is one of the biggest determinants of improving patient health and needs, as well as decreasing general health care costs. Hence, with the application of deep learning, there has been a total change in the methods of medical diagnosis. This scoping review provides an employment of the current research progress of deep learning in different disease types, such as cancer, neurodegenerative diseases, and ophthalmologic diseases. This paper intends to compare different deep learning architectures, preprocessing methodologies used for data, and how data is incorporated into the framework, with a view of giving a glimpse of what researchers are currently doing in this fast-growing field, difficulties that are being encountered, as well as the future direction of the research domain being considered in this paper.

Tashkent University of Information Technology (UZ), Manav Rachna International Institute of Research and Studies (IN), Sharda University (IN)
Openalex Percentile: Top 2%
Artificial Intelligence in Healthcare
8.49
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Advancements in Deep Learning for Early Detection and Diagnosis Across Multiple Disease Domains — Vishal Jain, Ibrohimbek Yusupov, et al. · (2024) | TGRS Research Map | TGRS