Deep learning of texture and structural features for multiclass Alzheimer's disease classification

Dolph, Chester; Alam, Mahbubul; Shboul, Zeina A.; Samad, Manar D.; Iftekharuddin, Khan M.

doi:10.1109/ijcnn.2017.7966129

Cited by 43 publications

(20 citation statements)

References 34 publications

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“…In this work, the 3D topology of the brain has been considered as a whole in AD recognition which has resulted in an accurate recognition with a sensitivity value of 1 and specificity of 0.93. Dolph et al reported a model consisting of stacked AE (SAE) and DNN for multiclass classification that can learn complex non-linear atrophy patterns for classification of AD, MCI, and NC using both in-house and public-domain standardized CADDementia framework [40]. The authors produced two model specifications using blind datasets.…”

Section: Alzheimer's Diseasementioning

confidence: 99%

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

et al. 2020

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Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

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Section: Alzheimer's Diseasementioning

confidence: 99%

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

et al. 2020

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“…Though Dolph et al (2017) pioneered deep learning on this challenge, we are the first (to our knowledge) to propose an end-to-end training deep 3D CNN for the multiclass AD biomarker identification task in CADDementia. One of their systems ranked 7th, with 56.8% accuracy, while the other ranked 25th, tied with ADNet on 51.4%.…”

Section: Performancementioning

confidence: 99%

“…However, only binary classification tasks were considered, which were evaluated using cross-validation on ADNI. The first group to successfully propose a deep-learning approach to the CADDementia challenge (Dolph et al, 2017) extracted features such as cortical thickness, surface area, volumetric measurements, and texture. These values were used to greedily layer-wise train a stacked auto-encoder with three hidden layers, achieving competitive results.…”

Section: Introductionmentioning

confidence: 99%

Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI

Folego

Weiler

Casseb

et al. 2020

Front. Bioeng. Biotechnol.

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“…In [96], presented a multi-modal imaging marker using a new deep learning algorithm named randomized denoising autoencoder marker (rDAM) and ADNI MCI data to predict future cognitive and neural decline from FDG-PET, amyloid florbetapir PET, and structural MRI scans. While [97] used only stacked auto-encoder (SAE) deep neural networks (DNN) to classify AD from cognitive normal (CN) and mild cognitive impairment (MCI) in dementia patients.…”

Section: B Deep Learning Approaches For Dementia Diagnosismentioning

confidence: 99%

Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects

Ahmed

Zhang

Feng

et al. 2019

IEEE Rev. Biomed. Eng.

101

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Dementia, a chronic and progressive cognitive declination of brain function due to disease or impairment, is becoming more prevalent due to the aging population. A major challenge in dementia is achieving the accurate and timely diagnosis. In recent years, neuroimaging with computer-aided algorithms have made remarkable advances in addressing this challenge. Much of the success of these approaches can be attributed to the application of machine learning and deep learning techniques for neuroimaging. In this review paper, we present a comprehensive survey of automated diagnostic approaches for dementia using medical image analysis and machine learning algorithms published in the previous five years (2013-2018). Based on the rigorous review of the existing works, we have found that while most of the studies focused on Alzheimer's disease have demonstrated reasonable performance, the identification of other types of dementia remains a major challenge. Hybrid imaging analysis through deep learning approaches may hold promises for early diagnosis of these other types of dementia, and warrant further investigation in the future. The main contributions of this review paper are as follows: 1. Based on the detailed analysis of the existing literature, this paper discusses the most recent neuroimaging procedures for dementia diagnosis, and 2. It systematically explains the machine learning techniques and, in particular, deep learning approaches for early detection of dementia.

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Deep learning of texture and structural features for multiclass Alzheimer's disease classification

Cited by 43 publications

References 34 publications

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

Alzheimer's Disease Detection Through Whole-Brain 3D-CNN MRI

Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects

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