Abstract Class imbalance is often a problem in various real-world data sets, where one class (i.e. the minority class) contains a small number of data points and the other (i.e. the majority class) contains a large number of data points. It is notably difficult to develop an effective model using current data mining and machine learning algorithms without considering data preprocessing to balance the imbalanced data sets. Random undersampling and oversampling have been used in numerous studies to ensure that the different classes contain the same number of data points. A classifier ensemble (i.e. a structure containing several classifiers) can be trained on several different balanced data sets for later classification purposes. In this paper, we introduce two undersampling strategies in which a clustering technique is used during the data preprocessing step. Specifically, the number of clusters in the majority class is set to be equal to the number of data points in the minority class. The first strategy uses the cluster centers to represent the majority class, whereas the second strategy uses the nearest neighbors of the cluster centers. A further study was conducted to examine the effect on performance of the addition or deletion of 5 to 10 cluster centers in the majority class. The experimental results obtained using 44 small-scale and 2 large-scale data sets revealed that the clustering-based undersampling approach with the second strategy outperformed five state-of-the-art approaches. Specifically, this approach combined with a single multilayer perceptron classifier and C4.5 decision tree classifier ensembles delivered optimal performance over both small- and large-scale data sets.

分群式取樣法於類別不平衡問題之研究;Clustering-Based Under-sampling in Class Imbalanced Data

Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images

Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score. 

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Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Improvement of Bagging performance for classification of imbalanced datasets using evolutionary multi-objective optimization

The recent developments of deep learning support the identification and classification of lung diseases in medical images. Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images. There has only been one survey paper published in the last five years regarding deep learning directed at lung diseases detection. However, their survey is lacking in the presentation of taxonomy and analysis of the trend of recent work. The objectives of this paper are to present a taxonomy of the state-of-the-art deep learning based lung disease detection systems, visualise the trends of recent work on the domain and identify the remaining issues and potential future directions in this domain. Ninety-eight articles published from 2016 to 2020 were considered in this survey. The taxonomy consists of seven attributes that are common in the surveyed articles: image types, features, data augmentation, types of deep learning algorithms, transfer learning, the ensemble of classifiers and types of lung diseases. The presented taxonomy could be used by other researchers to plan their research contributions and activities. The potential future direction suggested could further improve the efficiency and increase the number of deep learning aided lung disease detection applications.

A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods

Most of the real-time image acquisitions produce noisy measurements of the unknown true images. Image denoising is the post-acquisition technique to improve the signal-to-noise ratio of the acquired images. Denoising is an essential pre-processing step for different image processing applications such as image segmentation, feature extraction, registration, and other quantitative measurements. Among different denoising methods proposed in the literature, the non-local means method is a preferred choice for images corrupted with an additive Gaussian noise. A conventional non-local means filter (CNLM) suppresses noise in a given image with minimum loss of structural information. In this paper, we propose modifications to the CNLM algorithm where the samples are selected statistically using Shapiro–Wilk test. The experiments on standard test images demonstrate the effectiveness of the proposed method.

Non-Local Means Image Denoising Using Shapiro-Wilk Similarity Measure

A novel relay selection algorithm using ant colony optimization with artificial noise for secrecy enhancement in cooperative networks

When data is produced in large quantities, classification requires proper techniques for data analysis and representation. Successful optimization techniques can be used to find the most informative features from the data. Feature selection enhances classification accuracy as it eliminates irrelevant and redundant data from the dataset. Swarm Intelligent algorithms, which are inspired by the social behaviour of living organisms are found to have good performance in feature selection. This paper investigates the possibility of enhancing the artificial bee colony algorithm, one of the efficient swarm intelligent techniques, by hybridizing it with the genetic algorithm. Experiments on various datasets prove that the proposed approach has better results than the existing techniques.

A Hybrid Feature Selection Approach Using Artificial Bee Colony and Genetic Algorithm

The study of the factors that influence the use of automated diagnosing system for Tuberculosis (TB) can help in developing a strategy to enhance health professional’s acceptance of a given new system. This paper aims to examine the factors influencing the adoption of Computer-assisted Medical Diagnosing system for TB by clinicians and lab technicians in the context of developing countries health care. For that, we conducted a qualitative study by collecting data through in-depth interviews of the clinicians and lab technicians. A total of 18 interviews were conducted and the collected data was analysed, using content analysis. The results show that human characteristics (age and computer knowledge), technological characteristics (performance expectancy and effort expectancy), organizational characteristics (training and support), and technological impact could influence the adoption of CMD system for TB. The findings of this study would help researchers and medical professionals in providing them more knowledge about technological adoption and also helps to determine the uptake of the recent technology by healthcare professionals.

M. K. Sabu

Papers

Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods

Non-Local Means Image Denoising Using Shapiro-Wilk Similarity Measure

A novel relay selection algorithm using ant colony optimization with artificial noise for secrecy enhancement in cooperative networks

A Hybrid Feature Selection Approach Using Artificial Bee Colony and Genetic Algorithm

Factors influencing the adoption of computerized medical diagnosing system for tuberculosis