Mutliresolutional ensemble PartialNet for Alzheimer detection using magnetic resonance imaging data. Issue 10 (18th February 2022)

• Record Type: Journal Article
• Authors: Razzak, Imran; Naz, Saeeda; Ashraf, Abida; Khalifa, Fahmi; Bouadjenek, Mohamed Reda; Mumtaz, Shahid
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 10(2022)
• Page Start: 6613
• Page End: 6630
• Publication Date: 2022-02-18
• Subjects: Alzheimer -- brain disorder -- DenseNet -- ensemble learning -- PartialNet; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22856
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Alzheimer's disease (AD) is an irreversible and progressive disorder where a large number of brain cells and their connections degenerate and die, eventually destroy the memory and other important mental functions that affect memory, thinking, language, judgment, and behavior. Not a single test can effectively determine AD; however, CT and magnetic resonance imaging (MRI) can be used to observe the decrease in size of different areas (mainly temporal and parietal lobes). This paper proposes an integrative deep ensemble learning framework to obtain better predictive performance for AD diagnosis. Unlike DenseNet, we present a multiresolutional ensemble PartialNet tailored to Alzheimer detection using brain MRIs. PartialNet incorporates the properties of identity mappings, diversified depth as well as deep supervision, thus, considers feature reuse that in turn results in better learning. Additionally, the proposed ensemble PartialNet demonstrates better characteristics in terms of vanishing gradient, diminishing forward flow with better training time, and a low number of parameters compared with DenseNet. Experiments performed on benchmark AD neuroimaging initiative data set that showed considerable performance gain (2 + % ↑ $\uparrow $ ) and (1.2 + % ↑ $\uparrow $ ) for multiclass and binary class in AD detection in comparison to state‐of‐the‐art methods.