An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

Chui, Kwok Tai; Gupta, Brij B.; Alhalabi, Wadee; Alzahrani, Fatma

doi:10.3390/diagnostics12071531

Cited by 52 publications

(17 citation statements)

References 35 publications

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“…This research mainly focuses on testing the effectiveness of the Trend-Following strategy in the Chinese commodity market. Presently researchers are proposing the use of machine learning [6] and deep learning [7] techniques for the prediction of trading. The balance of this paper is organized as follows: portfolio construction, data collection, and other microstructure issues are detailed in Section 2.…”

Section: Introductionmentioning

confidence: 99%

Research on the Performance of the Trend Following Trading Strategy in the Chinese Commodity Market

Xie

Huang

Bian

et al. 2022

Wireless Communications and Mobile Computing

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Trend following strategy is a popular strategy that investors often use in trading around the world. Stocks are bought during an upswing and sold during a decline, the two main phases of the trend-following trading technique. This research evaluates the performance of the trend-following strategy in the Chinese commodity market by systematically employing quantitative methods to trade and get back test results for performance evaluation. The main trading indicator for this research is DMAC (Dual Moving Average Crossover) with a trend indicator called ADX for adjustment. As a kind of technical analysis, so-called “Dual Moving Average Crossovers” are often cited as providing reliable signs for discerning future stock price movements. By employing these indicators and systematically backtesting on 21 commodity futures for ten years, the research discovers that DMAC does not perform well (negative annualized return and sharpe ratio) from 2011 to 2021. By refining the strategy which is to replace DMAC with MACD (Moving Average Convergence/Divergence) and abandon ADX, the backtest result performs much better. The refinement suggests that the utilization of different trading signals/indicators will lead to a different performance of the trend following strategy in the Chinese commodity market. The research concludes that the trend following strategy is worthy of exploring in the Chinese commodity market in terms of using different trading indicators.

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Section: Introductionmentioning

confidence: 99%

Research on the Performance of the Trend Following Trading Strategy in the Chinese Commodity Market

Xie

Huang

Bian

et al. 2022

Wireless Communications and Mobile Computing

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“…It is understood that there is room for improvement in our research work. We suggest future research directions with the ideas of (i) investigating the effectiveness of the heterogeneous datasets of different disciplines to enhance the knowledge transfer between source and target models [ 40 , 41 ]; (ii) investigating the extent of smoothing, downsampling, and fine-graining of the multi-scale scheme on the performance of the model; (iii) generating additional training data using the variants of generative adversarial networks [ 42 , 43 ] because downsampling sacrifices the available ground truth data [ 44 ]; (iv) generating other types of noise such as speckle noise and random noise in the images to study the robustness of the model [ 45 , 46 ]; and (v) evaluating more noise injection approaches such as rotation, cropping, and re-sizing.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection

Chui¹,

Gupta

Ran

et al. 2022

Cancers

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Background: Prostate cancer is the 4th most common type of cancer. To reduce the workload of medical personnel in the medical diagnosis of prostate cancer and increase the diagnostic accuracy in noisy images, a deep learning model is desired for prostate cancer detection. Methods: A multi-scale denoising convolutional neural network (MSDCNN) model was designed for prostate cancer detection (PCD) that is capable of noise suppression in images. The model was further optimized by transfer learning, which contributes domain knowledge from the same domain (prostate cancer data) but heterogeneous datasets. Particularly, Gaussian noise was introduced in the source datasets before knowledge transfer to the target dataset. Results: Four benchmark datasets were chosen as representative prostate cancer datasets. Ablation study and performance comparison between the proposed work and existing works were performed. Our model improved the accuracy by more than 10% compared with the existing works. Ablation studies also showed average improvements in accuracy using denoising, multi-scale scheme, and transfer learning, by 2.80%, 3.30%, and 3.13%, respectively. Conclusions: The performance evaluation and comparison of the proposed model confirm the importance and benefits of image noise suppression and transfer of knowledge from heterogeneous datasets of the same domain.

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“…Machine learning techniques can be used for the detection of many diseases [7]. This section illustrated feature selection and how to choose a suitable classifier for classifying the tumour section and normal section.…”

Section: Classifier Selection In Machine Learningmentioning

confidence: 99%

“…Whole-slide photos of histology lymph node sections are shown. Because most organizations use 0.9 as their threshold [7][8][9][10], this research will first attempt to threshold the tumour probability heatmap using 0.9. The texture features and morphological characteristics were the two primary groupings of features in this dataset.…”

Section: Classifier Selection In Machine Learningmentioning

confidence: 99%

“…When comparing SVM with random forest, the random forest still has some overfitting issues. In addition, to minimize overfitting when training the random forest classifier, one group extracted just four features from each heatmap, which was updated based on their previous work [7]. Histologically, there was no regional lymph node metastasis, and isolated tumour cells (ITC) were not examined separately pN0(i+) Histologically, there was no regional lymph node metastasis, immunohistochemistry was positive, and the tumour focus was ≤0.…”

Section: Support Vector Machine (Svm) Classificationmentioning

confidence: 99%

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CAMELYON 17 Challenge: A Comparison of Traditional Machine Learning (SVM) with the Deep Learning Method

Sun

Meng

Liu

2022

Wireless Communications and Mobile Computing

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The pathologist’s diagnosis is crucial in identifying and categorizing pathological cancer sections, as well as in the physician’s subsequent evaluation of the patient’s condition and therapy. It is recognised as the “gold standard”; however, both objective and subjective pathological diagnoses have limits, such as tissue corruption resulting from the nonstandard collection of diseased tissue, nonstandard tissue fixation or delivery, or a lack of necessary clinical data. In addition, diagnostic pathology encompasses too much information; thus, it requires time and effort to grow a trained pathologist. Consequently, computer-assisted diagnosis has become an essential tool for replacing or assisting pathologists with computer technology and graphical development. In this regard, the CAMELYON 17 competition was designed to identify the best algorithm for detecting cancer metastases in the lymph. Each participant was given 899 whole-slide photos for the development of their algorithms. More than 300 people enrolled on the competition. CAMELYON 17 is primarily focused on the categorization of lymph node metastases. The TNM classification system is the primary classification system. Participants at CAMELYON 17 mostly use categorization and learning techniques in deep learning and machine learning. In order to get a better understanding of the top-selected algorithms, we examine the advantages and limitations of traditional machine learning and deep learning for classifying breast cancer metastases.

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An MRI Scans-Based Alzheimer’s Disease Detection via Convolutional Neural Network and Transfer Learning

Cited by 52 publications

References 35 publications

Research on the Performance of the Trend Following Trading Strategy in the Chinese Commodity Market

Research on the Performance of the Trend Following Trading Strategy in the Chinese Commodity Market

Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection

CAMELYON 17 Challenge: A Comparison of Traditional Machine Learning (SVM) with the Deep Learning Method

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