CT-Scan imaging has been widely used in kidney diagnosis to estimate kidney size, shape, and position, provide information about kidney function, and help diagnose structural abnormalities like cysts, stones, and infection. However, the use of CT-Scan in kidney diagnosis is operator-dependent. The images may be interpreted differently depending on operators’ skills and experiences, variations in human perceptions of the images, and differences in features used in diagnosis. Chronic kidney disease diagnosis may be improved by implementing automated techniques and computer-aided diagnosis systems, but these have not been widely explored. Therefore, this study proposed that chronic kidney disease has been acquired using the Random Forest classifier with 96.33% accuracy among different Machine Learning classifiers. Overall, this study has shown promising results. Implementing these proposed algorithms into current chronic kidney disease diagnosis techniques may help improve current diagnosis accuracy while reducing human intervention and operator dependency.
Now we closed the publication phase and launched the post-publication phase of the conference. REVIEWWWERS'08 Brainstorming Workshop is Now Open from 2023-Jan-01 to 2023-Jan-31. MOL2NET Committee, Authors, and Validated Social Media Followers Worldwide are ... Invited to Post Moderated Questions/Answers, Comments, about papers. Please kindly post your public Answers (A) to the following questions in order to promote interchange of scientific ideas. These are my Questions (Q) to you:
Q1. Dear authors you report an interesting application of AI/ML to CT image analysis in Biomed Eng.
What is the source of your medical imaging data? Is it representative of different populations strata by age, etc.?
Dear author thanks in advance for your kind support answering the questions. Now, please become a verified REVIEWWWER of our conference by making questions to other papers in different Mol2Net congresses. Commenting Steps: Login, Go to Papers List, Select Paper, Write Comment, Click Post Comment. Papers list: https://mol2net-08.sciforum.net/presentations/view,
Workshop link: https://mol2net-08.sciforum.net/#reviewwwers
Yes populations was different by age.
Yes populations was different by age.
Yes populations was different by age
Yes populations was different by age
