1
Computer Science, Department of Mathematical and Physical Sciences, College of Arts and Sciences, University of Nizwa, Oman
(naresh@unizwa.edu.om)
2
Faculty of Computer Technology, Assam Down Town University, Guwahati, India
(gunikhan.sonowal@gmail.com)
3
Department of Computer Science and Engineeing, School of Technology, GITAM University, Bengaluru, India
(balajipucs@gmail.com)
Abstract :
Age-related macular degeneration (AMD) is the leading cause of permanent vision loss, and drusen is an early clinical sign in the progression of AMD. Early detection is key since that's when treatment is most effective. The eyes of someone with AMD need to be checked often. Ophthalmologists may detect illness by looking at a color picture of the fundus taken using a fundus camera. Ophthalmologists need a system to help them diagnose illness since the global elderly population is growing rapidly and there are not enough specialists to go around. Since drusen vary in size, form, degree of convergence, and texture, it is challenging to detect and locate them in a color retinal picture. Therefore, it is difficult to develop a Modified Continual Learning (MCL) classifier for identifying drusen. To begin, we use X-AI (Explainable Artificial Intelligence) in tandem with one of the Dual Tree Complex Wavelet Transform models to create captions summarizing the symptoms of the retinal pictures throughout all of the different stages of diabetic retinopathy. An Adaptive Neuro Fuzzy Inference System (ANFIS) is constructed using all nine of the pre-trained modules. The nine image caption models are evaluated using a variety of metrics to determine their relative strengths and weaknesses. After compiling the data and comparing it to many existing models, the best photo captioning model is selected. A graphical user interface was also made available for rapid analysis and data screening in bulk. The results demonstrated the system's potential to aid ophthalmologists in the early detection of ARMD symptoms and the severity level in a shorter amount of time.
Keywords :
Multiscale characteristics (MSC); Modified Continual Learning (MCL); Dual Tree Complex Wavelet Transform (DTCWT); Adaptive Neuro Fuzzy Inference System (ANFIS).
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| Style | # |
|---|---|
| MLA | Naresh Kumar, Gunikhan Sonowal , V. Balaji. "AMR-XAI-DWT: Age-Related Macular Regenerated Classification using X-AI with Dual Tree CWT." Fusion: Practice and Applications, Vol. 15, No. 2, 2024 ,PP. 17-35 (Doi : https://doi.org/10.54216/FPA.150202) |
| APA | Naresh Kumar, Gunikhan Sonowal , V. Balaji. (2024). AMR-XAI-DWT: Age-Related Macular Regenerated Classification using X-AI with Dual Tree CWT. Journal of Fusion: Practice and Applications, 15 ( 2 ), 17-35 (Doi : https://doi.org/10.54216/FPA.150202) |
| Chicago | Naresh Kumar, Gunikhan Sonowal , V. Balaji. "AMR-XAI-DWT: Age-Related Macular Regenerated Classification using X-AI with Dual Tree CWT." Journal of Fusion: Practice and Applications, 15 no. 2 (2024): 17-35 (Doi : https://doi.org/10.54216/FPA.150202) |
| Harvard | Naresh Kumar, Gunikhan Sonowal , V. Balaji. (2024). AMR-XAI-DWT: Age-Related Macular Regenerated Classification using X-AI with Dual Tree CWT. Journal of Fusion: Practice and Applications, 15 ( 2 ), 17-35 (Doi : https://doi.org/10.54216/FPA.150202) |
| Vancouver | Naresh Kumar, Gunikhan Sonowal , V. Balaji. AMR-XAI-DWT: Age-Related Macular Regenerated Classification using X-AI with Dual Tree CWT. Journal of Fusion: Practice and Applications, (2024); 15 ( 2 ): 17-35 (Doi : https://doi.org/10.54216/FPA.150202) |
| IEEE | Naresh Kumar, Gunikhan Sonowal, V. Balaji, AMR-XAI-DWT: Age-Related Macular Regenerated Classification using X-AI with Dual Tree CWT, Journal of Fusion: Practice and Applications, Vol. 15 , No. 2 , (2024) : 17-35 (Doi : https://doi.org/10.54216/FPA.150202) |