Triplet-Loss Based Siamese Convolutional Neural Network for 4-Way Classification of Alzheimer’s Disease

Shaffi, Noushath; Hajamohideen, Faizal; Mahmud, Mufti; Abdesselam, Abdelhamid; Subramanian, Karthikeyan; Sariri, Arwa Al

doi:10.1007/978-3-031-15037-1_23

Cited by 26 publications

(5 citation statements)

References 18 publications

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“…The work presented here is an extension of our previously published work [12]. This is our ongoing effort in researching Siamese architecture for AD detection.…”

Section: Introductionmentioning

confidence: 85%

“…In recent years artificial intelligence (AI) has been applied in diverse problem domains to solve various challenging problems, including text classification [13,14,15,16], cyber security [17,18,19,20], neurological disease detection [21,22,23,12] and management [24,25,26,27,28,29], elderly care [30,31], fighting pandemic [32,33,34,35,36,37,38], and healthcare service delivery [39,40,41]. In particular, deep learning (DL) has attracted a lot of attention [6,7].…”

Section: Related Workmentioning

confidence: 99%

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Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Hajamohideen

Shaffi

Mahmud

et al. 2022

Preprint

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Alzheimer's disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of the disease will reduce the suffering of the patients and their family members. Towards this aim, in this paper, we propose a Siamese Convolutional Neural Network (CNN) architecture that employs the triplet-loss function for the representation of input MRI images as k-dimensional embeddings. We used both pre-trained and non-pre-trained CNNs to transform images into the embedding space. These embeddings are subsequently used for the 4-way classification of Alzheimer's Disease. The model efficacy was tested using the ADNI and OASIS datasets which produced an accuracy of 91.83% and 93.85%, respectively. Furthermore, obtained results are compared with similar methods proposed in the literature.

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“…The work presented here is an extension of our previously published work [12]. This is our ongoing effort in researching Siamese architecture for AD detection.…”

Section: Introductionmentioning

confidence: 85%

Section: Related Workmentioning

confidence: 99%

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Hajamohideen

Shaffi

Mahmud

et al. 2022

Preprint

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“…Alzheimer's Disease (AD) is an untreatable, life-changing neurological sickness affecting the elderly population, leading to several hardships for the patients [1,2]. According to the latest World Alzheimer Report [3], about 55 million clinically diagnosed AD patients live worldwide, with an estimated rise of cases to 139 million by 2050 [3].…”

mentioning

confidence: 99%

“…AD patients will have to endure many difficulties, such as memory loss, behavioural disturbances, vision, and mobility complications that interfere with daily routine tasks [1,4,5]. These sufferings will increase to the extent of interfering with one's ability to lead a self-reliant personal and social life and causing numerous tribulations for the caretaking family members [2,6].…”

mentioning

confidence: 99%

“…disorder detection and management [2,[33][34][35][36][37][38][39][40][41][42][43][44], supporting the detection and management of the COVID-19 pandemic [45][46][47][48][49][50][51][52], elderly monitoring and care [53][54][55][56], cyber security and trust management [57][58][59][60][61][62], various disease diagnosis [63][64][65][66][67][68][69], smart healthcare service delivery [70][71][72], text and social media mining [73][74][75][76], personalised learning [77][78][79][80], earthquake detection…”

mentioning

confidence: 99%

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Explainable Artificial Intelligence in Alzheimer’s Disease Classification: A Systematic Review

Viswan,

Shaffi,

Mahmud

et al. 2023

Cogn Comput

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The unprecedented growth of computational capabilities in recent years has allowed Artificial Intelligence (AI) models to be developed for medical applications with remarkable results. However, a large number of Computer Aided Diagnosis (CAD) methods powered by AI have limited acceptance and adoption in the medical domain due to the typical blackbox nature of these AI models. Therefore, to facilitate the adoption of these AI models among the medical practitioners, the models' predictions must be explainable and interpretable. The emerging field of explainable AI (XAI) aims to justify the trustworthiness of these models' predictions. This work presents a systematic review of the literature reporting Alzheimer's disease (AD) detection using XAI that were communicated during the last decade. Research questions were carefully formulated to categorise AI models into different conceptual approaches (e.g., Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, Global, Local etc.) and frameworks (Local Interpretable Model-Agnostic Explanation or LIME, SHapley Additive exPlanations or SHAP, Gradient-weighted Class Activation Mapping or GradCAM, Layer-wise Relevance Propagation or LRP, etc.) of XAI. This categorisation provides broad coverage of the interpretation spectrum from intrinsic (e.g., Model-Specific, Ante-hoc models) to complex patterns (e.g., Model-Agnostic, Post-hoc models) and by taking local explanations to a global scope. Additionally, different forms of interpretations providing in-depth insight into the factors that support the clinical diagnosis of AD are also discussed. Finally, limitations, needs and open challenges of XAI research are outlined with possible prospects of their usage in AD detection.

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Machine Learning Models to Analyze the Effect of Drugs on Neonatal-ICU Length of Stay

Adiba

Rahman

2022

Communications in Computer and Information Science

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Triplet-Loss Based Siamese Convolutional Neural Network for 4-Way Classification of Alzheimer’s Disease

Cited by 26 publications

References 18 publications

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Four-Way Classification of Alzheimer's Disease using Deep Siamese Convolutional Neural Network with Triplet-Loss Function

Explainable Artificial Intelligence in Alzheimer’s Disease Classification: A Systematic Review

Machine Learning Models to Analyze the Effect of Drugs on Neonatal-ICU Length of Stay

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