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DOI: 10.14569/IJACSA.2022.0130139
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Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data

Author 1: Palwinder Kaur
Author 2: Rajesh Kumar Singh

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 1, 2022.

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Abstract: 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.

Keywords: Content-based image retrieval system; CLAHE; Gabor filter; Cuckoo search; LION optimization; support vector machine

Palwinder Kaur and Rajesh Kumar Singh, “Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data” International Journal of Advanced Computer Science and Applications(IJACSA), 13(1), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130139

@article{Kaur2022,
title = {Feature Selection Pipeline based on Hybrid Optimization Approach with Aggregated Medical Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130139},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130139},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {1},
author = {Palwinder Kaur and Rajesh Kumar Singh}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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