Diagnosis of Abdominal Diseases Affecting Major Organs Using CT Image and YOLOV8
K. Ramanandhini1, Pandiarajan S2
1Mrs. K. Ramanandhini, Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India.
2Mr. Pandiarajan S, Department of Computer Science, Kalaignarkarunanidhi Institute of Technology, Kannampalayam (Tamil Nadu), India.
Manuscript received on 21 November 2024 | First Revised Manuscript received on 27 November 2024 | Second Revised Manuscript received on 14 December 2024 | Manuscript Accepted on 15 January 2025 | Manuscript published on 30 January 2025 | PP: 17-19 | Volume-5 Issue-2, January 2025 | Retrieval Number: 100.1/ijpmh.B105005020125 | DOI: 10.54105/ijpmh.B1050.05020125
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This study investigates the YOLOv8 method, a popular object detection model, to detect abnormalities in abdominal CT scans. Our study leverages the sophisticated architecture and point-of- care detection capabilities of YOLOv8 to show that the model improves diagnostic accuracy and helps radiologists quickly identify potential panic cases
Keywords: YOLOv8 Method, Abdominal Diseases, CT Scans, Radiologists.
Scope of the Article: YOLOv8 Method