ISSN2320-2882
Impact Factor9.17
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ESTD2013
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Paper Title
AI-Based Early Detection of Chronic Diseases Using Medical Imaging: Use deep learning to detect early signs of diseases like cancer or diabetes from X-rays or MRI scans.
Authors
Pooja Pawar , Ashwini Phalkhe , Kiran Unhale , Aditi Jagdale , Aaman Havaldar, VinayKhule
Keywords
AI-Based Early Detection of Chronic Diseases Using Medical Imaging: Use deep learning to detect early signs of diseases like cancer or diabetes from X-rays or MRI scans.
Abstract
Chronic disease significantly affects health on a global scale. Deep machine learning algorithms have found widespread application in the diagnosis of chronic diseases. Early diagnosis and treatment reduce the chance of a disease getting worse and, as a result, raise related mortality. Applying this approach to a dataset of optical coherence tomography images, we demonstrate performance comparable to that of human experts in classifying age related macular degeneration and diabetic macular edema. We also provide a more transparent and interpretable diagnosis by highlighting the regions recognized by the neural network. This paper proposes a final year project that develops and evaluates deep-learning pipelines to detect early signs of chronic diseases from medical imagining modalities. The study includes dataset collection and curation, image preprocessing, model design, performance evaluation and explainability.
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IJCRT — journal style
"AI-Based Early Detection of Chronic Diseases Using Medical Imaging: Use deep learning to detect early signs of diseases like cancer or diabetes from X-rays or MRI scans.", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.13, Issue 10, page no.65-68, October 2025, Available :https://ijcrt.org/papers/IJCRTBH02014.pdf
APA — 7th edition
Pawar, P., Phalkhe, A., Unhale, K., Jagdale, A., & Aaman Havaldar, V. (2025). AI-Based Early Detection of Chronic Diseases Using Medical Imaging: Use deep learning to detect early signs of diseases like cancer or diabetes from X-rays or MRI scans. International Journal of Creative Research Thoughts, 13(10), 65-68. https://ijcrt.org/viewpaperforall.php?paper=IJCRTBH02014
MLA — 9th edition
Pawar, Pooja, et al. "AI-Based Early Detection of Chronic Diseases Using Medical Imaging: Use deep learning to detect early signs of diseases like cancer or diabetes from X-rays or MRI scans." International Journal of Creative Research Thoughts, vol. 13, no. 10, 2025, pp. 65-68, https://ijcrt.org/viewpaperforall.php?paper=IJCRTBH02014.
Chicago — 17th, bibliography
Pawar, Pooja, et al. "AI-Based Early Detection of Chronic Diseases Using Medical Imaging: Use deep learning to detect early signs of diseases like cancer or diabetes from X-rays or MRI scans." International Journal of Creative Research Thoughts 13, no. 10 (2025): 65-68. https://ijcrt.org/viewpaperforall.php?paper=IJCRTBH02014.
Harvard — author–date
Pawar, P. et al. (2025) 'AI-Based Early Detection of Chronic Diseases Using Medical Imaging: Use deep learning to detect early signs of diseases like cancer or diabetes from X-rays or MRI scans', International Journal of Creative Research Thoughts, 13(10), pp. 65-68. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRTBH02014
IEEE — numbered reference
P. Pawar, A. Phalkhe, K. Unhale, A. Jagdale and V. Aaman Havaldar, "AI-Based Early Detection of Chronic Diseases Using Medical Imaging: Use deep learning to detect early signs of diseases like cancer or diabetes from X-rays or MRI scans," IJCRT, vol. 13, no. 10, pp. 65-68, Oct. 2025.
Vancouver — biomedical
Pawar P, Phalkhe A, Unhale K, Jagdale A, Aaman Havaldar V. AI-Based Early Detection of Chronic Diseases Using Medical Imaging: Use deep learning to detect early signs of diseases like cancer or diabetes from X-rays or MRI scans. IJCRT. 2025 Oct;13(10):65-68.
AMA — 11th edition
Pawar P, Phalkhe A, Unhale K, Jagdale A, Aaman Havaldar V. AI-Based Early Detection of Chronic Diseases Using Medical Imaging: Use deep learning to detect early signs of diseases like cancer or diabetes from X-rays or MRI scans. IJCRT. 2025;13(10):65-68. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRTBH02014
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRTBH02014,
author = {Pooja Pawar and Ashwini Phalkhe and Kiran Unhale and Aditi Jagdale and VinayKhule Aaman Havaldar},
title = {AI-Based Early Detection of Chronic Diseases Using Medical Imaging: Use deep learning to detect early signs of diseases like cancer or diabetes from X-rays or MRI scans.},
journal = {International Journal of Creative Research Thoughts},
volume = {13},
number = {10},
pages = {65-68},
year = {2025},
month = {October},
issn = {2320-2882},
url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRTBH02014}
}
RIS — EndNote, RefWorks
TY - JOUR
AU - Pawar, Pooja
AU - Phalkhe, Ashwini
AU - Unhale, Kiran
AU - Jagdale, Aditi
AU - Aaman Havaldar, VinayKhule
TI - AI-Based Early Detection of Chronic Diseases Using Medical Imaging: Use deep learning to detect early signs of diseases like cancer or diabetes from X-rays or MRI scans.
T2 - International Journal of Creative Research Thoughts
JA - IJCRT
VL - 13
IS - 10
PY - 2025
SN - 2320-2882
UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRTBH02014
SP - 65
EP - 68
ER -
Issue
Volume 13 Issue 10,
October-2025
Pages : 65-68
Article Information
- Published In
- International Journal of Creative Research Thoughts — Volume 13, Issue 10, October 2025
- Pages
- 65-68
- Published
- October 2025
- Registration ID
- IJCRT_295200
- Published Paper ID
- IJCRTBH02014
- Research Area
- Science and Technology
- Author Type
- Indian Author
- Location
- India
- Peer Review
- Request Peer Review Report
- Licence
- CC BY 4.0 · Open Access
- ISSN & Impact Factor
- 2320-2882 · IF 9.17
- Downloads
- 291
- Permanent Link
- https://ijcrt.org/viewpaperforall.php?paper=IJCRTBH02014
- Published Paper PDF
- https://ijcrt.org/papers/IJCRTBH02014.pdf
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International Journal of Creative Research Thoughts
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2320-2882
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The authors would like to thank the reviewers and the editorial board of International Journal of Creative Research Thoughts for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.
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