Correction: Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model

In the article “Diabetic Retinopathy Diagnosis Using Interval Neutrosophic Segmentation with Deep Learning Model” by V. Thanikachalam, M. G. Kavitha and V. Sivamurugan (Computer Systems Science and Engineering, 2023, Vol. 44, No. 3, pp. 2129–2145. DOI: 10.32604/csse.2023.026527). The following References [3], [4], [5], [6], [12] are irrelevant to the topic.

The authors wish to apologize for any inconvenience caused due to the fact that these references are irrelevant to the topic. Please check the following updates:

Original Content/Reference:

1. Delete References [3], [4], [5], [6], [12]:

3. S. Mahajan and A. K. Pandit, “Hybrid method to supervise feature selection using signal processing and complex algebra techniques,” Multimedia Tools and Applications, 2021. https://doi.org/10.1007/s11042-021-11474-y.

4. E. S. Madhan, S. Neelakandan, and R. Annamalai, “A novel approach for vehicle type classification and speed prediction using deep learning,” Journal of Computational and Theoretical Nano Science, vol. 17, no. 5, pp. 2237–2242, 2020.

5. J. Uthayakumar, N. Metawa, K. Shankar, and S. K. Lakshmanaprabu, “Intelligent hybrid model for financial crisis prediction using machine learning techniques,” Information Systems and e-Business Management, vol. 18, no. 4, pp. 617–645, 2020.

6. S. Arjunan and P. Sujatha, “Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol,” Applied Intelligence, vol. 48, no. 8, pp. 2229–2246, 2018.

12. A. Haldorai, A. Ramu, and C. O. Chow, “Editorial: Big data innovation for sustainable cognitive computing,” Mobile Networks and Applications, vol. 24, no. 1, pp. 221–223, 2019.

2. Delete content referencing References [3], [4], [5], [6], [12] in the main text:

“Physical inactivity, overweight, and unhealthy diet are the major reasons that fuel the occurrence of Type 2 diabetes (T2D) [3].”

“There are two phases in diabetic eye disease/retinopathy such as Proliferative DR (PDR) and Non-PDR (NPDR). DR can be diagnosed through optical coherence tomography and fluorescein angiography [4].”

“Machine Learning (ML) technique has been widely applied in the recent years to predict the occurrence, onset, progression and stages of different several diseases [5,6].”

“Some morphological tasks utilized the intensity level of the image as threshold for image segmentation [12].”

The authors state that the scientific conclusions are unaffected. This correction was approved by the Computer Systems Science and Engineering Editorial Office. The original publication has also been updated.