Authors: Ashish K. Patil, Ankita K. Sonawane , Vaishnavi G. Chaudhari, Yatish M. Borase, Aniket S. Thale
DOI : 10.46335/IJIES.2025.10.8.10
Abstract – One of the most fascinating areas of research in recent years has been learning about lung diseases and how they are characterized. Given the numerous applications of medical imaging in healthcare facilities, illnesses, and diagnostic facilities, the size of medical imaging datasets is rapidly growing as well in order to capture hospital disorders. Even though this particular topic has been the subject of extensive investigation, this field remains complex and difficult. There are numerous methods for categorizing medical photographs in the literature. The primary flaw with conventional approaches is the semantic gap between the high-level semantic information that humans perceive and the low-level visual information that imaging technologies gather. Due to the challenge of organizing and querying the vast datasets, a novel process known as deep convolutional.
Received on: 09 May,2025 Revised on: 15 June,2025 Published on: 17 June,2025
