An intelligent medical image classification system using few‐shot learning. (15th November 2022)

• Record Type: Journal Article
• Authors: Abbas, Qaisar
• Is Part Of: Concurrency and computation. Volume 35:Number 2(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-15
• Subjects: deep learning -- few‐shot learning -- machine learning -- medical image classification -- transfer learning; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7451
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Due to the wide range of diseases and imaging modalities, a retrieving system is a challenging task to access the corresponding clinical cases from a large medical repository on time. Several intelligent systems are developed to recognize medical images based on various machine learning algorithms such as deep learning and transfer learning methods that have outperformed compared to traditional methods. However, they are time‐consuming and required more labeled data for training the network models. To overcome these issues, an intelligence medical imaging recognition (IMR‐FSL) system is developed based on three phases of the few‐short learning (FSL) concept, which required few labeled examples to train the model and they are capable to generalize on huge and unseen medical datasets. To test and train the performance of the proposed IMR‐FSL system, many imaging modalities and multiclass datasets such as TCIA and Kvasir are utilized. Statistical analysis is also performed to measure the performance of the IMR‐FSL system. On average, the IMR‐FSL system achieved SE of 95%, SP of 96.5%, ACC of 97.5%, and AUC of 0.96 on both datasets. The performance of the IMR‐FSL system demonstrates the effectiveness of other state‐of‐the‐art systems.