An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images

Bäckström, Karl; Nazari, Mahmood; Gu, Irene Yu‐Hua; Jakola, Asgeir Store

doi:10.1109/isbi.2018.8363543

Cited by 99 publications

(73 citation statements)

References 7 publications

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“…The skull stripping/brain extraction is a preliminary step in MRI analysis. A pre-processing step of trim edges (TE) has also been reported in the same context [36]. Skull stripping (SST, BE) Skull stripping/brain extraction is one of the most important pre-processing steps for eliminating non-brain tissues from brain MR images.…”

Section: Stripping/trimmingmentioning

confidence: 99%

“…Intensity normalization (IN) Intensity normalization is used to reduce the intensity variation caused due to usage of different scanners or parameters for scanning different subjects or the same subject at disparate time [36,40].…”

Section: Normalization (Nm)mentioning

confidence: 99%

“…Cortical reconstruction (CR) Cortical reconstruction is required for quantitative analysis of human brain structure [36].…”

Section: Linear Regression (Lr)mentioning

confidence: 99%

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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: Stripping/trimmingmentioning

confidence: 99%

Section: Normalization (Nm)mentioning

confidence: 99%

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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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“…DNNs make it possible to solve several practical tasks in real life, such as detecting cancer [17,31,32,84] or Alzheimer's [9,60,61]. Furthermore, DNN studies of facial properties can reveal several diseases, sexual orientation, IQ, and political preferences.…”

Section: Relations Between Ai and Ami Super-ai And Super-amimentioning

confidence: 99%

“…Among many others, some examples of the latest developments of deep-learning methods for AD detection include DeepAD [60], which resulted in a 98.84% accuracy on a relatively small dataset, by randomly partitioning the MRI dataset where each subject can contain several scans in a different time. The authors of [9] proposed a simple, yet effective, 3D convolutional network (3D-CNN) architecture and tested it on a larger dataset with a training/test set resulting in a 98.74% test accuracy. Further tests on using a subjectbased training/test set partition strategy resulted in a 93.26% test accuracy for AD detection.…”

Section: Ami For Assisting Medical Diagnosis: Brain Tumours and Alzhementioning

confidence: 99%

Artificial intelligence and ambient intelligence

Gams

Härmä

et al. 2019

AIS

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Ambient intelligence (AmI) is intrinsically and thoroughly connected with artificial intelligence (AI). Some even say that it is, in essence, AI in the environment. AI, on the other hand, owes its success to the phenomenal development of the information and communication technologies (ICTs), based on principles such as Moore's law. In this paper we give an overview of the progress in AI and AmI interconnected with ICT through information-society laws, superintelligence, and several related disciplines, such as multi-agent systems and the Semantic Web, ambient assisted living and e-healthcare, AmI for assisting medical diagnosis, ambient intelligence for e-learning and ambient intelligence for smart cities. Besides a short history and a description of the current state, the frontiers and the future of AmI and AI are also considered in the paper.

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Applying software‐defined ambient intelligence techniques for cognitive and emotional awareness in the Internet of Things

Azeez Syed,

Nag,

Basu

et al. 2023

Int J Communication

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SummarySmart “discrete” technologies are taking over everyday activities with an intended shift towards complete automation. With this, ambient intelligence (AmI) has come under the limelight, to make these routines subtle and automated, enabled through the widespread adoption of the ubiquitous and pervasive Internet of Things (IoT). AmI is context‐driven depending largely on user input through various interfaces, which is nothing but the interplay between IoT and artificial intelligence (AI). Despite extensive incorporation of personal digital assistants and smart home systems, AI is not yet capable of accurately interpreting and responding to human emotions. This paper aims to present one such application to gauge the emotional and cognitive state of the user and respond appropriately, be it ambient lighting or an emergency SOS. The proposed model extracts network packets to work with audio messages on the real‐time transport protocol (RTP) layer and presents a lightweight multilayer perceptron (MLP)‐based speech emotion recognition (SER) model for emotional analysis with an accuracy of 80.52%. It also features a simple confidence rating system for further optimization.

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An efficient 3D deep convolutional network for Alzheimer's disease diagnosis using MR images

Cited by 99 publications

References 7 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

Artificial intelligence and ambient intelligence

Applying software‐defined ambient intelligence techniques for cognitive and emotional awareness in the Internet of Things

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