ARCHIVES
Review Article
A Review of Deep Learning Approach for Classification and Efficacy Testing of Chhattisgarh Herbal Ingredients in Skincare
B Janhavi1
Dr. Shanu Kuttan Rakesh2
1 M.Tech. Scholar, Department of Computer Science and Engineering, Chouksey Engineering College, Bilaspur (C.G.), India. 2 Associate Professor, Department of Computer Science and Engineering, Chouksey Engineering College, Bilaspur (C.G.), India.
Published Online: May-August 2026
Pages: 846-851
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502091
References
1. J. Deng et al., “ImageNet: A large-scale hierarchical image database,” in Proc. CVPR, 2009.
2. A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. NeurIPS, 2012.
3. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2015.
4. O. Russakovsky et al., “ImageNet large scale visual recognition challenge,” International Journal of Computer Vision, vol. 115, pp. 211–
252, 2015.
5. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc. ICLR, 2015.
6. S. Mohanty et al., “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, 2016.
7. J. Redmon et al., “You only look once: Unified, real-time object detection,” in Proc. CVPR, 2016.
8. K. He et al., “Deep residual learning for image recognition,” in Proc. CVPR, 2016.
9. S. Han et al., “Deep compression: Compressing deep neural networks,” in Proc. ICLR, 2016.
10. G. Huang et al., “Densely connected convolutional networks,” in Proc. CVPR, 2017.
11. A. Howard et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv: 1704.04861, 2017.
12. N. K. Sinha et al., “Medicinal plants of India and their traditional uses,” Journal of Ethnopharmacology, 2017.
13. T. Tschandl et al., “HAM10000 dataset for dermatoscopic image analysis,” Scientific Data, 2018.
14. A. Kamilaris and F. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, 2018.
15. F. Chollet, Deep Learning with Python, 2018.
16. M. Sandler et al., “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proc. CVPR, 2018.
17. S. A. Chouhan et al., “Transfer learning approach for plant disease detection,” Computers and Electronics in Agriculture, 2019.
18. M. Tan and Q. Le, “EfficientNet: Rethinking model scaling for CNNs,” in Proc. ICML, 2019.
19. P. Warden et al., TinyML: Machine Learning with TensorFlow Lite on Microcontrollers, O’Reilly, 2019.
20. M. Sandler et al., “MobileNetV3,” in Proc. ICCV, 2019.
21. H. Esteva et al., “Dermatologist-level classification of skin cancer,” Nature, 2017.
22. J. Redmon and A. Farhadi, “YOLOv3: An incremental improvement,” arXiv: 1804.02767, 2018.
23. T. Tschandl et al., “The HAM10000 dataset,” Scientific Data, 2018.
24. A. Bochkovskiy et al., “YOLOv4: Optimal speed and accuracy,” arXiv: 2004.10934, 2020.
25. A. Kumar et al., “Skin disease classification using deep CNN,” Biomedical Signal Processing and Control, 2021.
26. P. Jain et al., “Deep learning-based medicinal plant classification,” IEEE Access, 2021.
27. S. Singh et al., “CNN-based plant leaf recognition,” Pattern Recognition Letters, 2020.
28. R. Raj et al., “Deep learning in healthcare applications: A review,” IEEE Reviews in Biomedical Engineering, 2021.
29. A. Verma et al., “Transfer learning for medical image classification,” IEEE Access, 2022.
30. A. Sharma et al., “AI-based herbal plant identification systems,” Expert Systems with Applications, 2022.
31. S. Chatterjee et al., “AI in agriculture and healthcare systems,” 2020.
32. S. Liu et al., “Lightweight object detection models for edge devices,” Artificial Intelligence Review, 2024.
33. TensorFlow Team, “TensorFlow documentation,” 2024.
34. Keras Team, “Keras API reference,” 2024.
35. OpenCV Team, “OpenCV documentation,” 2024.
36. TinyML Foundation, “TinyML overview and applications,” 2023.
37. S. Chandra et al., “Edge AI for healthcare applications,” IEEE Access, 2023.
38. J. Lin et al., “Feature pyramid networks for object detection,” in Proc. CVPR, 2017.
39. S. Han et al., “Deep compression for neural networks,” in Proc. ICLR, 2016.
40. J. Redmon and A. Farhadi, “YOLOv2: Better, faster, stronger,” 2017.
2. A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. NeurIPS, 2012.
3. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, pp. 436–444, 2015.
4. O. Russakovsky et al., “ImageNet large scale visual recognition challenge,” International Journal of Computer Vision, vol. 115, pp. 211–
252, 2015.
5. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Proc. ICLR, 2015.
6. S. Mohanty et al., “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, 2016.
7. J. Redmon et al., “You only look once: Unified, real-time object detection,” in Proc. CVPR, 2016.
8. K. He et al., “Deep residual learning for image recognition,” in Proc. CVPR, 2016.
9. S. Han et al., “Deep compression: Compressing deep neural networks,” in Proc. ICLR, 2016.
10. G. Huang et al., “Densely connected convolutional networks,” in Proc. CVPR, 2017.
11. A. Howard et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications,” arXiv: 1704.04861, 2017.
12. N. K. Sinha et al., “Medicinal plants of India and their traditional uses,” Journal of Ethnopharmacology, 2017.
13. T. Tschandl et al., “HAM10000 dataset for dermatoscopic image analysis,” Scientific Data, 2018.
14. A. Kamilaris and F. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, 2018.
15. F. Chollet, Deep Learning with Python, 2018.
16. M. Sandler et al., “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proc. CVPR, 2018.
17. S. A. Chouhan et al., “Transfer learning approach for plant disease detection,” Computers and Electronics in Agriculture, 2019.
18. M. Tan and Q. Le, “EfficientNet: Rethinking model scaling for CNNs,” in Proc. ICML, 2019.
19. P. Warden et al., TinyML: Machine Learning with TensorFlow Lite on Microcontrollers, O’Reilly, 2019.
20. M. Sandler et al., “MobileNetV3,” in Proc. ICCV, 2019.
21. H. Esteva et al., “Dermatologist-level classification of skin cancer,” Nature, 2017.
22. J. Redmon and A. Farhadi, “YOLOv3: An incremental improvement,” arXiv: 1804.02767, 2018.
23. T. Tschandl et al., “The HAM10000 dataset,” Scientific Data, 2018.
24. A. Bochkovskiy et al., “YOLOv4: Optimal speed and accuracy,” arXiv: 2004.10934, 2020.
25. A. Kumar et al., “Skin disease classification using deep CNN,” Biomedical Signal Processing and Control, 2021.
26. P. Jain et al., “Deep learning-based medicinal plant classification,” IEEE Access, 2021.
27. S. Singh et al., “CNN-based plant leaf recognition,” Pattern Recognition Letters, 2020.
28. R. Raj et al., “Deep learning in healthcare applications: A review,” IEEE Reviews in Biomedical Engineering, 2021.
29. A. Verma et al., “Transfer learning for medical image classification,” IEEE Access, 2022.
30. A. Sharma et al., “AI-based herbal plant identification systems,” Expert Systems with Applications, 2022.
31. S. Chatterjee et al., “AI in agriculture and healthcare systems,” 2020.
32. S. Liu et al., “Lightweight object detection models for edge devices,” Artificial Intelligence Review, 2024.
33. TensorFlow Team, “TensorFlow documentation,” 2024.
34. Keras Team, “Keras API reference,” 2024.
35. OpenCV Team, “OpenCV documentation,” 2024.
36. TinyML Foundation, “TinyML overview and applications,” 2023.
37. S. Chandra et al., “Edge AI for healthcare applications,” IEEE Access, 2023.
38. J. Lin et al., “Feature pyramid networks for object detection,” in Proc. CVPR, 2017.
39. S. Han et al., “Deep compression for neural networks,” in Proc. ICLR, 2016.
40. J. Redmon and A. Farhadi, “YOLOv2: Better, faster, stronger,” 2017.
Related Articles
2026
Artificial Intelligence in Learning and Teaching
2026
Admin Assist: An AI – Driven Configuration and Orchestration for Enterprise Application
2026
Enhancing Blood Group Identification using pigeon inspired optimization: An Innovative Approach
2026
Eco-Genius: Power Up Smart, Power Down Waste
2026
Crowd-Sourced Disaster Response and Rescue Assistant
2026
Unveiling Deepfake Detection Using Vision Transformers: A Survey and Experimental Study
Share Article
Or copy link
https://www.indjcst.com/archives/a-review-of-deep-learning-approach-for-classification-and-efficacy-testing-of-chhattisgarh-herbal-ingredients-in-skincare
*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.