Advancements in Medical Imaging: Detecting Kidney Stones in CT Scans using a ELM-I AdaBoost-RT Model
- Title
- Advancements in Medical Imaging: Detecting Kidney Stones in CT Scans using a ELM-I AdaBoost-RT Model
- Creator
- Sudarsan M.S.V.D.; Sapra P.; Suganthi D.; Jeevitha S.; Ankit R.; Chauhan A.
- Description
- Kidney stones have been more common in recent years, leading many to believe that the condition is common. The condition's strong relationship with other terrible diseases makes it a major threat to public health. The development of instruments and procedures that facilitate the diagnosis and treatment of this ailment has the potential to enhance the effectiveness and efficiency of health care. Preprocessing, feature extraction, level set segmentation, and model training are the four steps that make up this approach. Part of the preprocessing includes eliminating the skeletal skeleton and soft-organs. Level set segmentation is commonly used for object tracking, motion segmentation, and image segmentation. An extremely effective feature extraction method called Gray level co-occurrence matrix (GLCM) is suggested for extracting the necessary characteristics from the segmented image. That ELM-I-AdaBoost-RT was used all during training. This cutting-edge technique achieves an average accuracy of 95.83%, surpassing both ELM and AdaBoost. 2024 IEEE.
- Source
- International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- computed tomography (CT); detection of kidney stones; extreme learning machine (ELM)
- Coverage
- Sudarsan M.S.V.D., Deemed To Be University, College Siddhartha Academy of Higher Education(SAHE), Department of Mathematics V.R. Siddhartha Engineering, Vijayawada, India; Sapra P., Rayat Bahra University, Department of Cse, Punjab, Mohali, India; Suganthi D., Saveetha University, Saveetha College of Arts and Sciences, Department of Computational Intelligence, Chennai, India; Jeevitha S., Kalasalingam Academy of Research and Education, Department of Computer Science and Information Technology, Krishnankoil, India; Ankit R., Lovely Professional University, School of Physical Education, Punjab, Phagwara, India; Chauhan A., Christ University, Department of Life Sciences, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835036066-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Sudarsan M.S.V.D.; Sapra P.; Suganthi D.; Jeevitha S.; Ankit R.; Chauhan A., “Advancements in Medical Imaging: Detecting Kidney Stones in CT Scans using a ELM-I AdaBoost-RT Model,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19088.