Overview on segmentation and classification for the Alzheimer’s disease detection from brain MRI

Gulhare, Kajal Kiran; Shukla, Shruti; Sharma, Lokesh Kumar

doi:10.14445/22312803/ijctt-v43p119

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…An ideal analysis is not feasible until medium to critical cortex loss has happened. By consuming image processing approaches [2], Alzheimer locale can take by using a blend of denoising, feature extraction, segmentation and classification techniques. The proposed pre-processing technique has the capability of supporting in medicinal examination.…”

Section: Introductionmentioning

confidence: 99%

A Novel Pre-Processing Approach for the Denoising of Alzheimer Disease Image Dataset

Mohananthini

Sathiamoorthy

2021

AJCST

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Medical imaging doing an indispensable part in the area of medicine. Noise in the image is maddening as it worsens the quality of image. Thus, removal of noise is perpetually a problematic work in the images of all domain. Alzheimer’s disease, a neurological dysfunction in which destruction of cells in brain creates mental weakening and memory loss. The distinguished reason for Alzheimer’s disease is low brain activity and low blood flow. We proposed a framework for removal of noise in Alzheimer disease image using histogram equalization, thresholding, open morphological operation and a wavelet transform. This framework reduces the noise and significantly better than the existing methods used for Alzheimer disease images.

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Section: Introductionmentioning

confidence: 99%

A Novel Pre-Processing Approach for the Denoising of Alzheimer Disease Image Dataset

Mohananthini

Sathiamoorthy

2021

AJCST

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Section: Introductionmentioning

confidence: 99%

“…The purpose of segmenting images is to remove unwanted information in order to locate meaningful objects from the processed images. The classification is used to produce meaningful patterns from raw data, classify them into different groups based on their characteristics and predict new patterns based on previous knowledge [8].In advance of the machine learning paradigm new learning methods have been reported in recent years. Deep learning methodology is attracting the researchers in the field of machine learning.…”

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confidence: 99%

Deep Neural Network Classification method to Alzheimer?s Disease Detection

Gulhare

Shukla²,

Sharma

2017

IJARCSSE

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Abstract-Early detection of Alzheimer disease (AD) is important for the management of disease. The human brainMagnetic resonance imaging (MRI) data have been used to detection of Alzheimer disease detection. The detection of AD is quite challenging and thus an automated tool to classify AD can be useful. Deep learning can make major advances in solving such problems. In this study, the longitudinal MRI data in non-demented and demented older adults data is utilized and the image processing technique was adopted for the data segmentation and attribute selection. Finally, deep neural network (DNN) classification is implemented for AD detection. The DNN 96.6% correctly identified AD and the minimum error rate obtained from a DNN. It shows the DNN will be useful for the development of improved computer aided diagnosis tool in MRI data.Keywords-Deep Neural Network, Alzheimer Disease, MRI Images, Classification I. INTRODUCTIONThe dementia is a global issue and that the effects of the future epidemic will be felt predominantly in low and middleincome countries. It was estimated that 46.8 million people worldwide were living with dementia in 2015 and this number will almost double every 20 years, to 87.88 million in 2035, 116.78 million in 2045 and 131.5 million in 2050. It has also a huge economic impact. The worldwide cost of dementia was estimated US$ 817.9 billion in the year of 2015 and it is projected the costs in 2030 will be around US$ 2 trillion [1]. Alzheimer"s disease (AD) is the most common cause of dementia associated with aging [2]; it accounts for 64 percent of all dementias [3]. AD is a growing public health problem among the elderly in developing countries, whose aging population is increasing rapidly [4]. AD is characterized by a progressive decline in cognitive function. AD is substantially increased among people aged 65 years or more, with a progressive decline in memory, thinking, language and learning capacity. AD should be differentiated from normal age-related decline in cognitive function, which is more gradual and associated with less disability. The disease often starts with mild symptoms and ends with severe brain damage. People with dementia lose their abilities at different rates [5]. The detection of AD in early and accurate is beneficial for the management of disease. Neuroimaging, such as magnetic resonance imaging (MRI) or computed tomography (CT) and with single photon emission computed tomography (SPECT) or positron emission tomography (PET) can be used to help exclude other cerebral pathology or subtypes of dementia. It may predict conversion from prodromal to Alzheimer"s disease.[6] [7]. Medical image processing and machine learning tools can help neurologists in assessing whether a subject is developing the Alzheimer disease. The image segmentation and classification is an important task in MRI data analysis for the AD detection. Image segmentation is intended to partition images into well-defined regions, where each region is a set of pixels that share the same range of intensitie...

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Dementia diagnosis in young adults: a machine learning and optimization approach

Talaat,

Ibraheem

2024

Neural Comput & Applic

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Individuals who are younger and have dementia often start experiencing its symptoms before they turn 65, with cases even documented in people as young as their thirties. Researchers strive for accurate dementia diagnosis to slow or halt its progression. This paper presents a novel Enhanced Dementia Detection and Classification Model (EDCM) comprised of four modules: data acquisition, preprocessing, hyperparameter optimization, and feature extraction/classification. Notably, the model uses texture information from segmented brain images for improved feature extraction, leading to significant gains in both binary and multi-class classification. This is achieved by selecting optimal features via a Gray Wolf Optimization (GWO)-driven enhancement model. Results demonstrate substantial accuracy improvements after optimization. For instance, using an Extra Tree Classifier for "normal" cases, the model achieves 85% accuracy before optimization. However, with GWO-optimized features and hyperparameters, the accuracy jumps to 97%.

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Overview on segmentation and classification for the Alzheimer’s disease detection from brain MRI

Cited by 6 publications

References 9 publications

A Novel Pre-Processing Approach for the Denoising of Alzheimer Disease Image Dataset

A Novel Pre-Processing Approach for the Denoising of Alzheimer Disease Image Dataset

Deep Neural Network Classification method to Alzheimer?s Disease Detection

Dementia diagnosis in young adults: a machine learning and optimization approach

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