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Author: search.filters.author.Choomchuay, SomsakSubject: search.filters.subject.Random forest

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    Comparison of Sampling Methods for Imbalanced Data Classification in Random Forest
    (2019-01-10)
    Paing, May Phu
    ;
    Pintavirooj, C.
    ;
    Tungjitkusolmun, Supan
    ;
    Choomchuay, Somsak
    ;
    Hamamoto, Kazuhiko
    Imbalanced data classification is a serious and challenging task for most of the medical image diagnosis applications. They usually produce a larger number of false samples compared to the actual ones. That is the number of samples for the class of interest (minority) is significantly fewer than other types of class (majority). The classification performed using such data is called imbalanced data classification. As a consequence, the learning model bias towards the majority class and fails the classification of the minority class. Data sampling and ensemble methods are common ways to compensate for this issue. Random forest (RF), an ensemble of multiple decision trees, is very famous in both of the classification and regression problems because of its robust and accurate predictions. However, it also suffers class bias in the imbalanced data classification problems. This paper proposes and compares different sampling methods to solve the imbalanced data classification in RF.
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    Detection and classification of overlapping cell nuclei in cytology effusion images using a double-strategy random forest
    (2018-09-11)
    Win, Khin Yadanar
    ;
    Choomchuay, Somsak
    ;
    Hamamoto, Kazuhiko
    ;
    Raveesunthornkiat, Manasanan
    Due to the close resemblance between overlapping and cancerous nuclei, the misinterpretation of overlapping nuclei can affect the final decision of cancer cell detection. Thus, it is essential to detect overlapping nuclei and distinguish them from single ones for subsequent quantitative analyses. This paper presents a method for the automated detection and classification of overlapping nuclei from single nuclei appearing in cytology pleural effusion (CPE) images. The proposed system is comprised of three steps: nuclei candidate extraction, dominant feature extraction, and classification of single and overlapping nuclei. A maximum entropy thresholding method complemented by image enhancement and post-processing was employed for nuclei candidate extraction. For feature extraction, a new combination of 16 geometrical and 10 textural features was extracted from each nucleus region. A double-strategy random forest was performed as an ensemble feature selector to select the most relevant features, and an ensemble classifier to differentiate between overlapping nuclei and single ones using selected features. The proposed method was evaluated on 4000 nuclei from CPE images using various performance metrics. The results were 96.6% sensitivity, 98.7% specificity, 92.7% precision, 94.6% F1 score, 98.4% accuracy, 97.6% G-mean, and 99% area under curve. The computation time required to run the entire algorithm was just 5.17 s. The experiment results demonstrate that the proposed algorithm yields a superior performance to previous studies and other classifiers. The proposed algorithm can serve as a new supportive tool in the automated diagnosis of cancer cells from cytology images.