Ensemble-classifiers-assisted detection of cerebral microbleeds in brain MRI

Ateeq, Tayyab; Majeed, Muhammad Nadeem; Anwar, Syed Muhammad; Maqsood, Muazzam; Rehman, Zahoor Ur; Lee, Jong Weon; Muhammad, Khan; Wang, Shuihua; Mehmood, Irfan

doi:10.1016/j.compeleceng.2018.02.021

Cited by 55 publications

(37 citation statements)

References 15 publications

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“…There are various methods reported in literature related to brain diseases that are based on both conventional features and DL based methods [7] [24] [25] [15] [9]. In [58], authors combines fully convolutional neural network with conditional random field (CRF).…”

Section: Radiomics Using Deep Learningmentioning

confidence: 99%

A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

Anwar

Altaf

Rafique

et al. 2020

Radiomics and Radiogenomics in Neuro-Oncology

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Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology. Radiomics provide assistance in diagnosis of cancer, planning of treatment strategy, and prediction of survival. Radiomics in neuro-oncology has progressed significantly in the recent past. Deep learning has outperformed conventional machine learning methods in most image-based applications. Convolutional neural networks (CNNs) have seen some popularity in radiomics, since they do not require hand-crafted features and can automatically extract features during the learning process. In this regard, it is observed that CNN based radiomics could provide state-of-the-art results in neuro-oncology, similar to the recent success of such methods in a wide spectrum of medical image analysis applications. Herein we present a review of the most recent best practices and establish the future trends for AI enabled radiomics in neuro-oncology.

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Section: Radiomics Using Deep Learningmentioning

confidence: 99%

A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

Anwar

Altaf

Rafique

et al. 2020

Radiomics and Radiogenomics in Neuro-Oncology

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“…There have been a lot of evaluation metrics used for foreign exchange prediction but mean absolute error (MAE) and root mean squared error (RMSE) are the most common metrics [54][55][56][57][58]. These metrics can be evaluated as explained below.…”

Section: Evaluation Metricsmentioning

confidence: 99%

An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System

et al. 2019

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Financial time series analysis is an important research area that can predict various economic indicators such as the foreign currency exchange rate. In this paper, a deep-learning-based model is proposed to forecast the foreign exchange rate. Since the currency market is volatile and susceptible to ongoing social and political events, the proposed model incorporates event sentiments to accurately predict the exchange rate. Moreover, as the currency market is heavily dependent upon highly volatile factors such as gold and crude oil prices, we considered these sensitive factors for exchange rate forecasting. The validity of the model is tested over three currency exchange rates, which are Pak Rupee to US dollar (PKR/USD), British pound sterling to US dollar (GBP/USD), and Hong Kong Dollar to US dollar (HKD/USD). The study also shows the importance of incorporating investor sentiment of local and foreign macro-level events for accurate forecasting of the exchange rate. We processed approximately 5.9 million tweets to extract major events’ sentiment. The results show that this deep-learning-based model is a better predictor of foreign currency exchange rate in comparison with statistical techniques normally employed for prediction. The results present evidence that the exchange rate of all the three countries is more exposed to events happening in the US.

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“…[5][6][7] The processing of medical big data [8][9][10] is also important in helping to discover more information about the disease. 4 Even worse, CMBs can also be found from healthy elderly individuals.…”

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confidence: 99%

“…Therefore, it is necessary to detect CMBs in an early stage to help people live a healthy, untroubled, and happy life. [5][6][7] The processing of medical big data [8][9][10] is also important in helping to discover more information about the disease. Due to the paramagnetic property of Hemosiderin, CMB can be visually inspected when a magnetic field is applied, such as the T2-gradient recalled echo imaging, 11 three-dimensional T2-GRE, and susceptibility weighted imaging (SWI) that is the most sensitive one to date.…”

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Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling

Wang

Sun

Mehmood

et al. 2019

Concurrency and Computation

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Cerebral micro-bleedings are small chronic brain hemorrhages caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke at memory clinics and even healthy elderly people. CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. Considering that CMBs severely influence people's life, it is necessary to identify the CMBs in an early stage to prevent from further deterioration and to help people live a healthy life. In this paper, we proposed using CNN with stochastic pooling for the CMB detection. CNN has good performance in image and video recognition, recommender system, and nature language processing. Based on the collected subject, the experiment result shows that the six-convolution layer and three fully-connected layer CNN, nine-layers in total, achieved sensitivity, specificity, accuracy, and precision as 97.22%, and 97.35%, 97.28%, and 97.35% in average of ten runs, which shows better performance than five state-of-the-art methods. KEYWORDScerebral micro-bleeding, convolution neural network, detection, intra-cerebral hemorrhage, stochastic pooling, stroke INTRODUCTIONCerebral micro-bleedings (CMBs) are small chronic brain hemorrhages, which can be caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke and memory clinics that is initially set up for research purposes. 1 CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. 2 Previous studies show that, in a range of 18% to 68%, patients with ischemic stroke/transient ischemic attack have a high prevalence of CMBs. There are 23% of prevalence in patients with first-ever ischemic stroke and 44% in patients with recurrent ischemic stroke reported by a previous systematic review. 3 The strong relationship with CMBs and increased risk of ICH suggests that the antithrombotic drugs Concurrency Computat Pract Exper. 2020;32:e5130. wileyonlinelibrary.com/journal/cpe to test the performance of the proposed structure of CNN based on the stochastic pooling, we compared different pooling methods, including average pooling, max pooling, and stochastic pooling. Meanwhile, we compared CNN with different convolution number and the state-of-the-art method for CMB detection. The experiment result shows that our designed structure of CNN based on the stochastic pooling achieved the best performance in sensitivity as 97.22%, specificity as 97.35%, accuracy as 97.28%, and precision as 97.35%.The limitation of our work is that we may need to move the window from top to bottom and from left to right. This is time-consuming. It is better to use concurrent convolution in the future studies, and we may need to replace the fully connected layers with conv layers. Another limitation is that we have two labels (CMB and ...

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Ensemble-classifiers-assisted detection of cerebral microbleeds in brain MRI

Cited by 55 publications

References 15 publications

A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-Oncology

An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System

Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling

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