Automated detection and extraction of skull from MR head images: Preliminary results

Göçeri, Evgin; Songul, Caner

doi:10.1109/ubmk.2017.8093370

Cited by 12 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Skull stripping is the removal of the skull from a 3D brain MRI. For quantitative morphometric studies, the skull is the non-brain tissue that functions as noise, lowering CNN classification performance ( Goceri and Songül, 2017 ). Aside from that, removing the skull from the brain enhances segmentation outcomes.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer’s Disease Using Structural MRI

Lim

Lai

Haiskin

et al. 2022

Front. Aging Neurosci.

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is an irreversible neurological disorder that affects the vast majority of dementia cases, leading patients to experience gradual memory loss and cognitive function decline. Despite the lack of a cure, early detection of Alzheimer’s disease permits the provision of preventive medication to slow the disease’s progression. The objective of this project is to develop a computer-aided method based on a deep learning model to distinguish Alzheimer’s disease (AD) from cognitively normal and its early stage, mild cognitive impairment (MCI), by just using structural MRI (sMRI). To attain this purpose, we proposed a multiclass classification method based on 3D T1-weight brain sMRI images from the ADNI database. Axial brain images were extracted from 3D MRI and fed into the convolutional neural network (CNN) for multiclass classification. Three separate models were tested: a CNN built from scratch, VGG-16, and ResNet-50. As a feature extractor, the VGG-16 and ResNet-50 convolutional bases trained on the ImageNet dataset were employed. To achieve classification, a new densely connected classifier was implemented on top of the convolutional bases.

show abstract

Section: Methodsmentioning

confidence: 99%

Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer’s Disease Using Structural MRI

Lim

Lai

Haiskin

et al. 2022

Front. Aging Neurosci.

View full text Add to dashboard Cite

show abstract

“…Deep learning methods have also attained futuristic heights for analysis of skin lesion in recent times such that digital photographs are used for diagnosis of skin diseases [14]. A popular deep learning method, namely, Deep Convolutional Neural Networks (DCNNs), possess the ability to process common and highly variable tasks in fine-grained objects which is an important characteristics of skin lesion images [15,16].…”

Section: Literature Reviewmentioning

confidence: 99%

Diagnosis of Melanoma Lesion Using Neutrosophic and Deep Learning

Banerjee¹,

Singh²,

Chakraborty³

et al. 2021

View full text Add to dashboard Cite

Melanoma is a kind of skin cancer which occurs due to too much exposure of melanocyte cells to the dangerous UV radiations, that gets damaged and multiplies uncontrollably. This is popularly known as malignant melanoma and is comparatively less heard of than certain other types of skin cancers; however it can be more detrimental as it swiftly spreads if not detected and attended at a primary stage. The differentiation between benign and melanocytic lesions sometimes may be confusing, but the symptoms of the disease can reasonably be discriminated by a profound investigation of its histopathological and clinical characteristics. In the recent past, Deep Convolutional Neural Networks (DCNNs) have advanced in accomplishing far better results. The necessity of the present day is to have faster and computationally efficient mechanisms for diagnosis of the deadly disease. This paper makes an effort to showcase a deep learning-based ‘Keras’ algorithm, which is established on the implementation of DCNNs to investigate melanoma from dermoscopic and digital pictures and provide swifter and more accurate result as contrasted to standard CNNs. The main highlight of this paper, basically stands in its incorporation of certain ambitious notions like the segmentation performed by a culmination of a moving straight line with a sequence of points and the application of the concept of triangular neutrosophic number based on uncertain parameters. The experiment was done on a total of 40,676 images obtained from four commonly available datasets— International Symposium on Biomedical Imaging (ISBI) 2017, International Skin Imaging Collaboration (ISIC) 2018, ISIC 2019 and ISIC 2020 and the end result received was indeed motivating. It attained a Jac score of 86.81% on ISIC 2020 dataset and 95.98%, 95.66% and 94.42% on ISBI 2017, ISIC 2018 and ISIC 2019 datasets, respectively. The present research yielded phenomenal output in most instances in comparison to the pre-defined parameters with the similar types of works in this field.

show abstract

“…A given brain image often cannot be directly applied to build a model, while researchers only focus on specific disease-related regions called regions of interest (ROI). Therefore, researchers extract skull [ 7 ] and then segment the brain image into pieces of ROI and quantify the unique properties from the ROIs, such as gray values, hippocampus, and cerebrospinal fluid. Also, intensity normalization approaches [ 8 , 9 ] have been applied before segmentations.…”

Section: Neuroimaging In Alzheimer's Diseasementioning

confidence: 99%

Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis

Qiu

Zhou

et al. 2021

View full text Add to dashboard Cite

Background: Recent development in neuroimaging and genetic testing technologies have made it possible to measure pathological features associated with Alzheimer's disease (AD) in vivo. Mining potential molecular markers of AD from high-dimensional, multi-modal neuroimaging and omics data will provide a new basis for early diagnosis and intervention in AD. In order to discover the real pathogenic mutation and even understand the pathogenic mechanism of AD, lots of machine learning methods have been designed and successfully applied to the analysis and processing of large-scale AD biomedical data. Objective: To introduce and summarize the applications and challenges of machine learning methods in Alzheimer's disease multi-source data analysis. Methods: The literature selected in the review is obtained from Google Scholar, PubMed, and Web of Science. The keywords of literature retrieval include Alzheimer's disease, bioinformatics, image genetics, genome-wide association research, molecular interaction network, multi-omics data integration, and so on. Conclusion: This study comprehensively introduces machine learning-based processing techniques for AD neuroimaging data and then shows the progress of computational analysis methods in omics data, such as the genome, proteome, and so on. Subsequently, machine learning methods for AD imaging analysis are also summarized. Finally, we elaborate on the current emerging technology of multi-modal neuroimaging, multi-omics data joint analysis, and present some outstanding issues and future research directions.

show abstract

Automated detection and extraction of skull from MR head images: Preliminary results

Cited by 12 publications

References 24 publications

Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer’s Disease Using Structural MRI

Deep Learning Model for Prediction of Progressive Mild Cognitive Impairment to Alzheimer’s Disease Using Structural MRI

Diagnosis of Melanoma Lesion Using Neutrosophic and Deep Learning

Applications and Challenges of Machine Learning Methods in Alzheimer's Disease Multi-Source Data Analysis

Contact Info

Product

Resources

About