A Comparison of Transfer Learning Performance Versus Health Experts in Disease Diagnosis from Medical Imaging
Open accessAbstract
Deep learning methods have huge success in task specific feature representation. Transfer learning algorithms are very much effective when large training data is scarce. It has been significantly used for diagnosis of diseases in medical imaging. This article presents a systematic literature review (SLR) by conducting a comparison of a variety of transfer learning approaches with healthcare experts in diagnosing diseases from medical imaging. This study has been compiled by reviewing research studies published in renowned venues between 2014 and 2019. Moreover, the data for the diagnosis performed by health care experts has also been acquired to perform a detailed comparative analysis for a wide range of diseases. The analysis has been performed on the basis of diseases, transfer learning approaches, type of medical imaging used. The comparative analysis is based on performance indices reported in studies which include diagnostic accuracy, true-positive (TP), false-positive (FP), true-negative (TN), false-negative (FN) sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). A total of5,188articles were identified out of which 63 studies were included. Among them 21 research studies contain sufficient data to construct the evaluation tables that enable process of test accuracy of transfer learning having sensitivity ranged from 71% to 100% (mean 85.25%) and specificity ranged from 64% to 100% (mean 81.92%). Furthermore, health experts having sensitivity ranged from 33% to 100% (mean 85.27%) and specificity ranged from 82% to 100% (mean 91.63%).This SLR found that diagnostic accuracy of transfer learning is approximately equivalent to the diagnosis of health experts. The results also revealed that convolutional neural networks (CNN) have been extensively used for disease diagnosis from medical imaging. Finally, inappropriate exposure of diseases in transfer learning studies restricts reliable elucidation of the outcomes of diagnostic accuracy.
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Articles were excluded based on criteria: not based on binary disease classification; diagnosing brain tumors without medical images; not identifying data sources or employing ambiguous methods of data collection; based on non-human samples.
“Articles were excluded based on the following criteria [21]: Research articles not based on binary disease classification.Research articles diagnosing brain tumors without medical images.Research articles not identifying data sources or employing ambiguous methods of data collection.Research articles based on non-human samples.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [21]
Classification of abnormal MR images into different tumor categories is still a challenging task.
“However, the classification of these abnormal images into different tumor categories is still a challenging task [21].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [21]
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- Authors
- Malik, H., Farooq, M. S., Khelifi, A., Abid, A., Nasir Qureshi, J., & Hussain, M.
- Affiliations
- University of Management and Technology (PK)
- Abu Dhabi University (AE)
- ORCID
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 139367-139386
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3037095325
- Cited by
- 104
- References
- 77
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- 2026: 8 · 2025: 15 · 2024: 10 · 2023: 12 · 2022: 15 · 2021: 42 · 2020: 2
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- 5.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- transfer learning, diagnostic imaging, disease diagnosis, diagnostic accuracy, convolutional neural networks, sensitivity and specificity, medical image classification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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