Breast cancer is a commonly occurring disease in women all over the world. Mammogram is an efficient technique used for screening and identification of abnormalities over the breast region. Earlier identification of breast cancer enhances the prognosis of patients and is mainly based on the experience of the radiologist in interpretation of mammogram with quality of image. The advent of Deep Learning (DL) and Computer Vision techniques is widely used to perform breast cancer diagnosis. This paper presents a new Optimal Multi-Level Thresholding-based Segmentation with DL enabled Capsule Network (OMLTS-DLCN) breast cancer diagnosis model utilizing digital mammograms. The OMLTS-DLCN model involves an Adaptive Fuzzy based median filtering (AFF) technique as a pre-processing step to eradicate the noise that exists in the mammogram images. Besides, Optimal Kapur's based Multilevel Thresholding with Shell Game Optimization (SGO) algorithm (OKMT-SGO) is applied for breast cancer segmentation. In addition, the proposed model involves a CapsNet based feature extractor and Back-Propagation Neural Network (BPNN) classification model is employed to detect the existence of breast cancer. The diagnostic outcomes of the presented OMLTS-DLCN technique is examined by means of benchmark Mini-MIAS dataset and DDSM dataset. The experimental values obtained highlights the superior performance of the OMLTS-DLCN model with a higher accuracy of 98.50 and 97.55% on the Mini-MIAS dataset and DDSM dataset, respectively.

Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images

Breast cancer is a common fatal disease for women. Early diagnosis and detection is necessary in order to improve the prognosis of breast cancer affected people. For predicting breast cancer, several automated systems are already developed using different medical imaging modalities. This paper provides a systematic review of the literature on artificial neural network (ANN) based models for the diagnosis of breast cancer via mammography. The advantages and limitations of different ANN models including spiking neural network (SNN), deep belief network (DBN), convolutional neural network (CNN), multilayer neural network (MLNN), stacked autoencoders (SAE), and stacked de-noising autoencoders (SDAE) are described in this review. The review also shows that the studies related to breast cancer detection applied different deep learning models to a number of publicly available datasets. For comparing the performance of the models, different metrics such as accuracy, precision, recall, etc. were used in the existing studies. It is found that the best performance was achieved by residual neural network (ResNet)-50 and ResNet-101 models of CNN algorithm.

Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review.

Background This paper provides a systematic review of the application of artificial intelligence (AI) in the form of machine learning (ML) and deep learning (DL) techniques in fighting against the effects of novel coronavirus disease (COVID-19). Objective & method The objective is to perform a scoping review on AI for COVID-19 using preferred reporting items of systematic reviews and meta-analysis (PRISMA) guidelines. A literature search was performed for relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050 research papers available in reputed publishers, a full-text review of 440 articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL, X-ray, and computed tomography (CT). Finally, 52 articles were included in the result synthesis of this paper. As part of the review, different ML regression methods were reviewed first in predicting the number of confirmed and death cases. Secondly, a comprehensive survey was carried out on the use of ML in classifying COVID-19 patients. Thirdly, different datasets on medical imaging were compared in terms of the number of images, number of positive samples and number of classes in the datasets. The different stages of the diagnosis, including preprocessing, segmentation and feature extraction were also reviewed. Fourthly, the performance results of different research papers were compared to evaluate the effectiveness of DL methods on different datasets. Results Results show that residual neural network (ResNet-18) and densely connected convolutional network (DenseNet 169) exhibit excellent classification accuracy for X-ray images, while DenseNet-201 has the maximum accuracy in classifying CT scan images. This indicates that ML and DL are useful tools in assisting researchers and medical professionals in predicting, screening and detecting COVID-19. Conclusion Finally, this review highlights the existing challenges, including regulations, noisy data, data privacy, and the lack of reliable large datasets, then provides future research directions in applying AI in managing COVID-19.

Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A Review.

CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images

This chapter focuses on the application of machine learning algorithms on the diagnosis of the novel coronavirus disease (COVID-19). First, data visualization is provided on increases in confirmed deaths and recovered cases of COVID-19 using currently available data from Johns Hopkins University. Next, the machine learning algorithms are used for the automatic diagnosis of COVID-19. Data-driven diagnosis is performed using a dataset of 5644 samples with 111 attributes provided by Hospital Israelita Albert Einstein, Brazil. As a preprocessing step, null values and categorical data are processed and standardization is performed. Next, feature selection is performed to find attributes that are most important for a COVID-19 diagnosis. A number of algorithms including random forest logistic regression, XGBoost, and decision tree are considered and their kernel parameters are optimized. The performance of classification algorithms is evaluated in terms of a number of factors including the testing accuracy, precision, recall, miss rate, receiver operating characteristic curve and area under the receiver operating characteristic curve. Experimental results show that serum glucose is the most influential attribute in predicting COVID-19. Our results also show that for the case of cross-validation, XGBoost has the highest accuracy value of 92.67% and logistic regressions have the second highest accuracy of 92.58%, whereas both XGBoost and LR have a 93% value for precision, recall, and F1 score. Moreover, for the case of the holdout method with 20% testing data, logistic regression with an accuracy of 94.06% outperforms other classifiers in terms of accuracy, precision, recall, and F1 score.

Application of machine learning for the diagnosis of COVID-19

Breast cancer is a common cancer for women. Early detection of breast cancer can considerably increase the survival rate of women. This paper mainly focuses on transfer learning process to detect breast cancer. Modified VGG (MVGG), residual network, mobile network is proposed and implemented in this paper. DDSM dataset is used in this paper. Experimental results show that our proposed hybrid transfers learning model (Fusion of MVGG16 and ImageNet) provides an accuracy of 88.3% where the number of epoch is 15. On the other hand, only modified VGG 16 architecture (MVGG 16) provides an accuracy 80.8% and MobileNet provides an accuracy of 77.2%. So, it is clearly stated that the proposed hybrid pre-trained network outperforms well compared to single architecture. This architecture can be considered as an effective tool for the radiologists in order to reduce the false negative and false positive rate. Therefore, the efficiency of mammography analysis will be improved.

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Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning

Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved.

/pdf/diagnosis-of-breast-cancer-based-on-modern-mammography-using-1d000actva.pdf

Diagnosis of breast cancer based on modern mammography using hybrid transfer learning

Impulse denoising based on noise accumulation and harmonic analysis techniques

Smart E-Learning is an education form, in which information technology plays an important role. Smart E-Learning utilizes the achievements of modern technologies such as artificial intelligence (AI). So, it can help to gain the ability of connectivity, sharing, convenience, and flexibility for learners. All activities including teaching, learning, discussing, testing, and grading, are well done over the internet environment. However, some potential risks can cause participants, especially, about data privacy. The issue is a big challenge for developing applications for smart E-Learning. Therefore, the goal to combine E-Learning with Blockchain technology to improve security for smart E-Learning is necessary. In this paper, we discuss potential and challenge for applying AI and Blockchain for developing applications for smart E-Learning in Vietnam. We also propose to combine independent testing models integrated with AI and Blockchain into the same smart E-Learning system.

AI and Blockchain: potential and challenge for building a smart E-Learning system in Vietnam

This study was conducted to apply supervised machine learning methods in opinion mining online customer reviews. First, the study automatically collected 39,976 traveler reviews on hotels in Vietnam on Agoda.com website, then conducted the training with machine learning models to find out which model is most compatible with the training dataset and apply this model to forecast opinions for the collected dataset. The results showed that Logistic Regression (LR), Support Vector Machines (SVM) and Neural Network (NN) methods have the best performance in opinion mining in Vietnamese language. This study is valuable as a reference for applications of opinion mining in the field of business.

Thai Kim Phung

Papers

Diagnosis of Breast Cancer Based on Modern Mammography using Hybrid Transfer Learning

Diagnosis of breast cancer based on modern mammography using hybrid transfer learning

Impulse denoising based on noise accumulation and harmonic analysis techniques

AI and Blockchain: potential and challenge for building a smart E-Learning system in Vietnam

A machine learning approach for opinion mining online customer reviews