M. P. Thiruvenkatasuresh^1,*, Surbhi Bhatia², Shakila Basheer³, Pankaj Dadheech⁴

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1767-1782, 2023, DOI:10.32604/iasc.2023.033711

Abstract The study aimed to apply to Machine Learning (ML) researchers working in image processing and biomedical analysis who play an extensive role in comprehending and performing on complex medical data, eventually improving patient care. Developing a novel ML algorithm specific to Diabetic Retinopathy (DR) is a challenge and need of the hour. Biomedical images include several challenges, including relevant feature selection, class variations, and robust classification. Although the current research in DR has yielded favourable results, several research issues need to be explored. There is a requirement to look at novel pre-processing methods to discard irrelevant features, balance the obtained… More >

Yanyi Wu¹, Xiaoyu Li², Qinsheng Zhu^1,*, Xiaolei Liu², Hao Wu¹, Shan Yang³

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6139-6149, 2022, DOI:10.32604/cmc.2022.032086

Abstract As an essential part of artificial intelligence, many works focus on image processing which is the branch of computer vision. Nevertheless, image localization faces complex challenges in image processing with image data increases. At the same time, quantum computing has the unique advantages of improving computing power and reducing energy consumption. So, combining the advantage of quantum computing is necessary for studying the quantum image localization algorithms. At present, many quantum image localization algorithms have been proposed, and their efficiency is theoretically higher than the corresponding classical algorithms. But, in quantum computing experiments, quantum gates in quantum computing hardware need… More >