Improved search space shrinking for medical image retrieval using capsule architecture and decision fusion. (1st June 2021)

• Record Type: Journal Article
• Authors: Bhattacharya, Jhilik; Bhatia, Tarunpreet; Pannu, Husanbir Singh
• Is Part Of: Expert systems with applications. Volume 171(2021)
• Publication Date: 2021-06-01
• Subjects: Content-based image retrieval -- Machine learning -- Image processing -- Image retrieval; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.114543
• Languages: English
• ISSNs: 0957-4174

Abstract

Graphical abstract: Highlights: A capsule classification system which significantly improves the top-1 accuracy. A decision fusion using RBC and W-DCT at the second step to refine the accuracy. IRMA error of 124.25 is obtained using 193 class-code categories. Abstract: Medical diagnosis is a challenging procedure that involves issues such as data imbalance, insufficient labels, obscure images, redundancy and lack of effective model training directions to shrink the semantic gap between human knowledge and computer algorithms. Due to privacy norms, sometimes medical images are difficult to access and therefore, retrieval of identical existing images from an existing repository is quite useful. This paper proposes a search space to narrow down the identical images in an archive by using (i) Capsule Networks, followed by a (ii) decision fusion with Wavelet-Discrete Cosine Transform (W-DCT) and Radon Barcodes (RBC). Empirical case study has been applied on IRMA (Image Retrieval in Medical Applications) dataset, ImageCLEFMed-2009, containing 14, 410 X-ray images, but the proposed method is generic, reproducible and scalable. Subjective and quantitative performance has been compared with the state-of-art and it has been found superior to yield accuracy of 92.83 % and IRMA error of 124.25 for 193 class-code category. Thus, the proof-of-concept helps to improves diagnosis efficiency for automatic image retrieval and annotation by clustering similar images from the underlying repository.