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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM22354

Digitization, Analysis & Prediction of Medical Reports Using Deep Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM22354

Volume:

7 |

6 |

Jun 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

RM

Rakesh Merugu

AM

Aryan Mantoo

AM

Aseem Manna

GM

Gaurav Meshram

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Abstract

Advancements in Deep Learning have enabled the healthcare industry to efficiently process and analyze a vast amount of medical data, including critical information about patients’ health. Medical reports, in particular, contain valuable data that can help identify trends, patterns, and anomalies in patients’ medical conditions, leading to better diagnosis and treatment plans. In this paper, we present an approach that utilizes deep learning algorithms, specifically CNN to predict, digitize, and analyze medical reports. An approach is proposed that uses OCR technology to digitize the reports, making it easier to analyze and store them in a digital format. This approach has the potential to improve the accuracy, efficiency, and quality of medical diagnosis and treatment, and can address challenges such as doctor prescription digitization, brain tumor classification, kidney stone classification, and skin disease classification. The approach is implemented in an Android application built using React Native, which enables healthcare providers to access these services through their smartphones.

KEYWORDS

Medical Reports Medical Services Medical Reports Analysis Human Diseases Deep Learning Prescription Digitization

Cite This Article

Rakesh Merugu, Aryan Mantoo, Aseem Manna, Gaurav Meshram, "Digitization, Analysis & Prediction of Medical Reports Using Deep Learning," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 6, Jun 2023. https://doi.org/10.55041/IJSREM22354

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Publication Date

5/5/2023

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Double-Blind Peer Review

Review Rounds: 2

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5

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The authors declare no conflict of interest related to this research work.

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