Artificial intelligence-based hybrid deep learning models for image classification: The first narrative review. (October 2021)

• Record Type: Journal Article
• Authors: Jena, Biswajit; Saxena, Sanjay; Nayak, Gopal K.; Saba, Luca; Sharma, Neeraj; Suri, Jasjit S.
• Is Part Of: Computers in biology and medicine. Volume 137(2021)
• Publication Date: 2021-10
• Subjects: Artificial intelligence -- Deep learning -- Hybrid deep learning -- Spatial -- Temporal -- Spatial-temporal -- Performance -- Risk-of-bias; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104803
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Artificial intelligence (AI) has served humanity in many applications since its inception. Currently, it dominates the imaging field—in particular, image classification. The task of image classification became much easier with machine learning (ML) and subsequently got automated and more accurate by using deep learning (DL). By default, DL consists of a single architecture and is termed solo deep learning (SDL). When two or more DL architectures are fused, the result is termed a hybrid deep learning (HDL) model. The use of HDL models is becoming popular in several applications, but no review of these uses has been designed thus far. Therefore, this study provides the first narrative HDL review by considering all facets of image classification using AI. Approach: Our review employs a PRISMA search strategy using Google Scholar, PubMed, IEEE, and Elsevier Science Direct, through which 127 relevant HDL studies were considered. Based on the computer vision evolution, HDLs were subsequently classified into three categories (spatial, temporal, and spatial-temporal). Each study was then analyzed based on several attributes, including continent, publisher, hybridization of two DL or ML, architecture layout, application type, data set type, dataset size, feature extraction methodology, connecting classifier, performance evaluation metrics, and risk-of-bias. Conclusion: The HDL models have shown stable and superior performance by taking the best aspects of two or more solo DL or fusion of DL with ML models. Our findings indicate that HDL is being applied aggressively to several medical and non-medical applications. Furthermore, risk-of-bias is highly debatable for DL and HDL models. Highlights: Use of PRISMA model for search strategy. Three types of hybrid deep learning architecture as: spatial, temporal, and spatial-temporal. Comparative analysis of three architectures for real-world applications. Linking HDL architecture with its performances with explanations. Statistical analysis on various HDL artificial intelligence attributes.