文章基本信息

标题：Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data
本地全文：下载
作者：Palwinder Kaur ; Rajesh Kumar Singh
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2022
卷号：13
期号：1
DOI：10.14569/IJACSA.2022.0130139
语种：English
出版社：Science and Information Society (SAI)
摘要：For quite some time, the usage of many sources of data (data fusion) and the aggregation of that data have been underappreciated. For the purposes of this study, trials using several medical datasets were conducted, with the results serving as a single aggregated source for identifying eye illnesses. It is proposed in this paper that a diagnostic system that can detect diabetic retinopathy, glaucoma, and cataract can be built as an alternative to current methods. The data fusion and data aggregation techniques used to create this multi-model system made it conceivable. As the name implies, it is a way of compiling data from a large number of legitimate sources. The development of a pipeline of algorithms was accomplished through iterative trials and hyper parameter tweaking. CLAHE (Contrast Level Adaptive Histogram Equalization) approaches, which increase the gradient between picture edges, improve segmentation by raising the contrast between picture edges. The Gabor filter has been shown to be the most effective method of selecting features. The Gabor filter was selected using a hybrid optimization method (LION + Cuckoo), which was developed by the author. For automation, the Support Vector Machine (SVM) radial is the most effective method since it delivers excellent stability and accuracy in terms of accuracy and recall, as well as precision and recall. The discoveries and approaches detailed here provide a more solid foundation for future image-based diagnostics researchers to build on in the future. Eventually, the findings of this study will help to improve healthcare workflows and practices.
关键词：Content-based image retrieval system; CLAHE; Gabor filter; Cuckoo search; LION optimization; support vector machine