Revolutionizing Diagnostic Insights: Exploring Advanced Image Processing Techniques and Neural Networks in Traditional Indian Medicine
Received: 11 September 2024 | Revised: 12 October 2024, 26 October 2024, and 2 November 2024| Accepted: 19 November 2024 | Online: 24 December 2024
Corresponding author: R. Srinivasan
Abstract
The Siddha and Ayurveda traditional Indian medicine practices utilize non-invasive diagnostic methods, such as Neikuri and Taila Bindu Pariksha, for patient diagnosis through urine analysis. While these methods have proven effective for centuries, their accuracy highly depends on the subjective experience of practitioners. To address this limitation, this study explores the use of advanced image processing techniques and deep learning, specifically Convolutional Neural Networks (CNNs), to automate and enhance diagnostic image analysis. This study utilized five pre-trained CNN models, namely DenseNet, ResNet, VGG-19, Inception, and EfficientNet, on a dataset of Neikuri images acquired from a Siddha medical institute, to standardize and improve the accuracy of patient diagnosis. The comparative evaluation revealed DenseNet as the best-performing model, achieving a classification accuracy of 93.33%, while Inception v3 followed with 90.5%. This study highlights the potential of integrating modern neural networks with traditional diagnostic practices, paving the way for more objective, efficient, and accessible healthcare solutions in traditional Indian medicine.
Keywords:
Convolutional Neural Networks (CNNs), deep learning, traditional medicine, Siddha, Ayurveda, urine test images, patient diagnosis, medical image analysis, Neikuri, Taila Bindu Pariksha, Mutra ParikshaDownloads
References
A. B. Abdul Bari, P. J. Samuel, M. D. Chandrasekar, R. Shyamala, and S. S. Rangasamy, "Towards Standardization–A New Protocol for Oil drop test (Neikuri) in Healthy Subjects," International Journal of Pharmacology and Clinical Sciences, vol. 4, no. 4, pp. 83–89, Feb. 2016.
K. B. Kachare and A. C. Kar, "Important aspect of Ayurvedic taila bindu pariksha to assesses disease prognosis," World Journal of Pharmaceutical Research, vol. 4, no. 2, pp. 851–860, Dec. 2014.
J. Jeyavenkatesh, S. R. Ramani, P. Saravanapandian, P. M. Bai, and R. S. Priya, "An Observational Cross-sectional Single Arm Trial and Perspective Study of Siddha Diagnostic Tool Neerkuri and Neikuri (Uroscopy) in COVID-19 Patients," Journal of Complementary and Alternative Medical Research, pp. 51–58, Sep. 2022.
G. Dhinesh Raman, "A Study on Documentation of Siddha Diagnostic Methods Specially Naadi, Neerkuri and Neikuri for Praana Vaadha Kurigal," Ph.D. dissertation, Government Siddha Medical College, Palayamkottai, India, 2019.
K. R. V. Darshini, "Comparative study of the Siddha Diagnostic Methods Specially Neerkuri & Neikuri with Modern Diagnostic Methods in Neerizhivu Madhumeham (Diabetes Mellitus–Type 2)," Ph.D. dissertation, Government Siddha Medical College, Palayamkottai, India, 2018.
V. Rohini, "A Study on Siddha Diagnostic Methodology of Neerkuri and Neikuri for" SOMA ROGAM"," Ph.D. dissertation, Government Siddha Medical College, Palayamkottai, India, 2022.
V. Dhivya, "A Clinical study on Documentation of Thaehiyin Ilakkanam compared with Neikuri in Siddha Science and Blood Grouping in Modern Science," Ph.D. dissertation, Government Siddha Medical College, Palayamkottai, India, 2019.
M. Wolfgram, "Truth Claims and Disputes in Ayurveda Medical Science," Journal of Linguistic Anthropology, vol. 20, no. 1, pp. 149–165, Jun. 2010.
R. Meena et al., "Fluorescent carbon dots driven from ayurvedic medicinal plants for cancer cell imaging and phototherapy," Heliyon, vol. 5, no. 9, Sep. 2019, Art. no. e02483.
A. Mukherjee, M. Banerjee, V. Mandal, A. C. Shukla, and S. C. Mandal, "Modernization of Ayurveda: A Brief Overview of Indian Initiatives," Natural Product Communications, vol. 9, no. 2, Feb. 2014, Art. no. 1934578X1400900239.
V. Patil and U. K. Sapra, "Clinical Diagnosis in Ayurveda: Concepts, Currnt Practice and Prospects," Journal of Ayurveda and Holistic Medicine (JAHM), vol. 1, no. 2, Apr. 2021.
S. Chen, Z. Sedghi Gamechi, F. Dubost, G. Van Tulder, and M. De Bruijne, "An end-to-end approach to segmentation in medical images with CNN and posterior-CRF," Medical Image Analysis, vol. 76, Feb. 2022, Art. no. 102311.
S. Atasever, N. Azginoglu, D. S. Terzi, and R. Terzi, "A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning," Clinical Imaging, vol. 94, pp. 18–41, Feb. 2023.
G. Litjens et al., "A survey on deep learning in medical image analysis," Medical Image Analysis, vol. 42, pp. 60–88, Dec. 2017.
S. Suganyadevi, V. Seethalakshmi, and K. Balasamy, "A review on deep learning in medical image analysis," International Journal of Multimedia Information Retrieval, vol. 11, no. 1, pp. 19–38, Mar. 2022.
H. P. Chan, R. K. Samala, L. M. Hadjiiski, and C. Zhou, "Deep Learning in Medical Image Analysis," in Deep Learning in Medical Image Analysis : Challenges and Applications, G. Lee and H. Fujita, Eds. Cham, Switzerland: Springer International Publishing, 2020, pp. 3–21.
A. S. Parihar and A. Java, "Densely connected convolutional transformer for single image dehazing," Journal of Visual Communication and Image Representation, vol. 90, Feb. 2023, Art. no. 103722.
A. Jaiswal, Deepali, and N. Sachdeva, "Empirical Analysis of Traffic Sign Recognition using ResNet Architectures," in 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India, May 2023, pp. 280–285.
G. Kılıçarslan, C. Koç, F. Özyurt, and Y. Gül, "Breast lesion classification using features fusion and selection of ensemble ResNet method," International Journal of Imaging Systems and Technology, vol. 33, no. 5, pp. 1779–1795, 2023.
R. K.P. and S. V., "Machine Learning Approach for Mixed type Wafer Defect Pattern Recognition by ResNet Architecture," in 2023 International Conference on Control, Communication and Computing (ICCC), Thiruvananthapuram, India, May 2023, pp. 1–6.
N. Shahadat and A. S. Maida, "Enhancing ResNet Image Classification Performance by Using Parameterized Hypercomplex Multiplication," in 2023 26th International Conference on Computer and Information Technology (ICCIT), Cox’s Bazar, Bangladesh, Dec. 2023, pp. 1–6.
S. Showkat and S. Qureshi, "Efficacy of Transfer Learning-based ResNet models in Chest X-ray image classification for detecting COVID-19 Pneumonia," Chemometrics and Intelligent Laboratory Systems, vol. 224, May 2022, Art. no. 104534.
R. Mohan, K. Ganapathy, A. Rama, "Brain tumour classification of Magnetic resonance images using a novel CNN based Medical Image Analysis and Detection network in comparison with VGG16," Journal of Population Therapeutics and Clinical Pharmacology, vol. 28, no. 2, Jan. 2022.
Z. Cao, J. Huang, X. He, and Z. Zong, "BND-VGG-19: A deep learning algorithm for COVID-19 identification utilizing X-ray images," Knowledge-Based Systems, vol. 258, Dec. 2022, Art. no. 110040.
Z. Hu, Z. Wang, Y. Jin, and W. Hou, "VGG-TSwinformer: Transformer-based deep learning model for early Alzheimer’s disease prediction," Computer Methods and Programs in Biomedicine, vol. 229, Feb. 2023, Art. no. 107291.
S. Anwar, S. R. Soomro, S. K. Baloch, A. A. Patoli, and A. R. Kolachi, "Performance Analysis of Deep Transfer Learning Models for the Automated Detection of Cotton Plant Diseases," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11561–11567, Oct. 2023.
Z. Li, L. Zhang, Z. Zhang, R. Xu, and D. Zhang, "Speckle classification of a multimode fiber based on Inception V3," Applied Optics, vol. 61, no. 29, pp. 8850–8858, Oct. 2022.
V. Ravi and R. Chaganti, "EfficientNet deep learning meta-classifier approach for image-based android malware detection," Multimedia Tools and Applications, vol. 82, no. 16, pp. 24891–24917, Jul. 2023.
X. Chen et al., "Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions," Cancer Medicine, vol. 12, no. 7, pp. 8690–8699, 2023.
A. Abdelrahman and S. Viriri, "EfficientNet family U-Net models for deep learning semantic segmentation of kidney tumors on CT images," Frontiers in Computer Science, vol. 5, Sep. 2023.
Downloads
How to Cite
License
Copyright (c) 2024 R. Srinivasan, Reeba Korah, M. Ravichandran
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
