Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)

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Deep Learning–Driven Disease Diagnosis Using Facial Image Analysis

Authors
D. Anjani Suputri Devi1, *, Suneetha Eluri2, Chinnam Sabitha3, D. Sasi Rekha1, Pentapati Kalyan Babu4, Naresh Konduri1, N. Mounika1
1Department of CSE, Sasi Institute of Technology & Engineering, Tadepalligudem, Andhra Pradesh, India
2Department of CSE, JNTUK, Kakinada, Andhra Pradesh, India
3Department of CSE, Research Scholar, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
4Department of IT, SRKR Engineering College, Bhimavaram, Andhra Pradesh, India
*Corresponding author. Email: anjanihasini@gmail.com
Corresponding Author
D. Anjani Suputri Devi
Available Online 31 December 2025.
DOI
10.2991/978-94-6463-940-7_18How to use a DOI?
Keywords
Deeplearnig; Gabor Filter; VGG16; SVM; LSTM; Diseases
Abstract

Deep Learning is a tool and it is utilized for to detect and categorize the images. Discovering diseases and taking measures to assist with human health is very advantageous. Unpredictable diseases are common these days. Diseases detectable from visible facial features can also help to curb further advancement of the disease state. There is a lot of data on the human face that assists with identifying and diagnosing medical conditions. Facial diagnosis may offer revolutionary medical diagnoses because it is non-invasive, cost-effective, and quick. Detecting and categorizing the diseases is what can be done. To assist in classifying diseases from facial features Gabor filter extracts the features and to classify the disease state we implemented VGG16, SVM and LSTM. This study has a 99% accuracy.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
Series
Advances in Intelligent Systems Research
Publication Date
31 December 2025
ISBN
978-94-6463-940-7
ISSN
1951-6851
DOI
10.2991/978-94-6463-940-7_18How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - D. Anjani Suputri Devi
AU  - Suneetha Eluri
AU  - Chinnam Sabitha
AU  - D. Sasi Rekha
AU  - Pentapati Kalyan Babu
AU  - Naresh Konduri
AU  - N. Mounika
PY  - 2025
DA  - 2025/12/31
TI  - Deep Learning–Driven Disease Diagnosis Using Facial Image Analysis
BT  - Proceedings of the Conference on Social and Sustainable Innovation in Technology & Engineering (SASI-ITE 2025)
PB  - Atlantis Press
SP  - 246
EP  - 257
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-940-7_18
DO  - 10.2991/978-94-6463-940-7_18
ID  - Devi2025
ER  -