Deep fusion pipeline for mild cognitive impairment diagnosis

Senanayake, Upul; Sowmya, Arcot; Dawes, Laughlin

doi:10.1109/isbi.2018.8363832

Cited by 29 publications

(17 citation statements)

References 10 publications

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“…Senanayake et al . [ 32 ] inspired by the concepts underlying the ResNet, DenseNet, and GoogleNet architectures, developed a model to classify MRI scans of subjects diagnosed with AD, MCI, and NC.…”

Section: Application Of Deep Learning Algorithms To Neuropsychiatric Disordersmentioning

confidence: 99%

“…They reported that the networks learned to accurately classify AD subjects from the NC, but had difficulty distinguishing them from E-MCI and L-MCI. Senanayake et al [32] inspired by the concepts underlying the ResNet, DenseNet, and GoogleNet architectures, developed a model to classify MRI scans of subjects diagnosed with AD, MCI, and NC.…”

Section: Cnns For Classification Of Neuropsychiatric Disordersmentioning

confidence: 99%

See 1 more Smart Citation

An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

Güney

Yigin

Guven

et al. 2021

Clin Psychopharmacol Neurosci

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Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage, and processing hardware, DL algorithms start dominating various fields including neuropsychiatry. There are many types of DL algorithms for different data types from survey data to functional magnetic resonance imaging scans. Because of limitations in diagnosing, estimating prognosis and treatment response of neuropsychiatric disorders; DL algorithms are becoming promising approaches. In this review, we aim to summarize the most common DL algorithms and their applications in neuropsychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture for their research.

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Section: Application Of Deep Learning Algorithms To Neuropsychiatric Disordersmentioning

confidence: 99%

Section: Cnns For Classification Of Neuropsychiatric Disordersmentioning

confidence: 99%

An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

Güney

Yigin

Guven

et al. 2021

Clin Psychopharmacol Neurosci

View full text Add to dashboard Cite

show abstract

“…Very recently, numerous studies have proposed to assist diagnosis of AD by means of CNNs (Aderghal et al, 2018(Aderghal et al, , 2017a(Aderghal et al, , 2017bBäckström et al, 2018;Basaia et al, 2019;Farooq et al, 2017;Gunawardena et al, 2017;Hon and Khan, 2017;Hosseini Asl et al, 2018;Zhang, 2018, 2017;Korolev et al, 2017;Lian et al, 2018;Li et al, , 2017Lin et al, 2018;Manhua Liu et al, 2018;Mingxia Liu et al, 2018a, 2018cQiu et al, 2018;Senanayake et al, 2018;Shmulev et al, 2018;Taqi et al, 2018;Valliani and Soni, 2017;Vu et al, 2018Vu et al, , 2017Wang et al, 2019Wang et al, , 2017Wu et al, 2018) . However, classification results among these studies are not directly comparable because they differ in terms of: i) sets of participants; ii) image preprocessing procedures, iii) cross-validation (CV) procedure and iv) reported evaluation metrics.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation

Wen

Thibeau–Sutre

Diaz-Melo

et al. 2020

Medical Image Analysis

524

401

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“…All the physicians' CUIs were also worse than the CPN system. The better results from our system are also noteworthy [ 30 , 31 ]. This occurs even though our model was simpler and uses less invasive critical criteria, at a cheaper costs and with possibilities of use in all clinical scopes, primary and specialized care.…”

Section: Resultsmentioning

confidence: 84%

A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture

Suárez-Araujo

Báez

Cabrera-León

et al. 2021

Computational and Mathematical Methods in Medicine

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Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and difficulties in understanding test cut-offs make diagnostic accuracy being low, particularly in primary care. Artificial neural networks (ANNs) are suitable to design computed aided diagnostic systems because of their features of generating relationships between variables and their learning capability. The main aim pursued in that work is to explore the ability of a hybrid ANN-based system in order to provide a tool to assist in the clinical decision-making that facilitates a reliable MCI estimate. The model is designed to work with variables usually available in primary care, including Minimental Status Examination (MMSE), Functional Assessment Questionnaire (FAQ), Geriatric Depression Scale (GDS), age, and years of education. It will be useful in any clinical setting. Other important goal of our study is to compare the diagnostic rendering of ANN-based system and clinical physicians. A sample of 128 MCI subjects and 203 controls was selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The ANN-based system found the optimal variable combination, being AUC, sensitivity, specificity, and clinical utility index (CUI) calculated. The ANN results were compared with those from medical experts which include two family physicians, a neurologist, and a geriatrician. The optimal ANN model reached an AUC of 95.2%, with a sensitivity of 90.0% and a specificity of 84.78% and was based on MMSE, FAQ, and age inputs. As a whole, physician performance achieved a sensitivity of 46.66% and a specificity of 91.3%. CUIs were also better for the ANN model. The proposed ANN system reaches excellent diagnostic accuracy although it is based only on common clinical tests. These results suggest that the system is especially suitable for primary care implementation, aiding physicians work with cognitive impairment suspicions.

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Deep fusion pipeline for mild cognitive impairment diagnosis

Cited by 29 publications

References 10 publications

An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation

A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture

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