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With the advent of precision medicine, biomarkers have recently come into focus as a promising tool for early cancer detection and treatment individualization. In particular, much interest has been shown in the oral microbiome as a promising potential cancer biomarker, especially for head and neck cancers. The American Cancer Society estimates that there will be nearly 50,000 new cases and roughly...
Machine learning classifiers help physicians to make near-perfect diagnoses, minimizing costs and time. Since medical data usually contains a high degree of uncertainty and ambiguity, proper ordering and classification require a proper comparative performance analysis of machine learning classifiers. Machine learning classifiers are applied on the Ovarian Cancer Dataset. Ovarian cancer is the fifth...
Computer-assisted analysis of endoscopic imagescan be helpful to the automatic diagnosis and classificationof neoplastic lesions. Barrett's esophagus (BE) is a commontype of reflux that is not straightforward to be detected byendoscopic surveillance, thus being way susceptible to erroneousdiagnosis, which can cause cancer when not treated properly. In this work, we introduce the Optimum-Path Forest...
Classification of different tumor type are of great significance in problems cancer prediction. Choosing the most relevant qualities from huge microarray expression is very important. It is a most explored subject in bioinformatics because of its hugeness to move forward humans understanding of inherent causing cancer mechanism. In this paper, we aim to classify leukaemia cells. Our approach relies...
This paper proposes a method for construction of classifiers for discharge summaries. First, morphological analysis is applied to a set of summaries and a term matrix is generated. Second, correspond analysis is applied to the classification labels and the term matrixand generates two dimensional coordinates. By measuring thedistance between categories and the assigned points, ranking of key wordswill...
The paper presents the results of research on the use of Deep Neural Networks (DNN) for automatic classification of the skin lesions. The authors have focused on the most effective kind of DNNs for image processing, namely Convolutional Neural Networks (CNN). In particular, three kinds of CNN were analyzed: VGG19, Residual Networks (ResNet) and the hybrid of VGG19 CNN with the Support Vector Machine...
This paper proposes a method for construction of classifiers for discharge summaries. First, morphological analysis is applied to a set of summaries and a term matrix is generated. Second, correspond analysis is applied to the classification labels and the term matrix and generates two dimensional coordinates. By measuring the distance between categories and the assigned points, ranking of key words...
Classification of lung cancer using a low population, high dimensional dataset is challenging due to insufficient samples to learn an accurate mapping among features and class labels. Current literature usually handles this task through hand-crafted feature creation and selection. In recent years, deep learning is found to be able to identify the underlying structure of data through the use of autoencoders...
Histopathology image classification can provide automated support towards cancer diagnosis. In this paper, we present a transfer learning-based approach for histopathology image classification. We first represent the image feature by Fisher Vector (FV) encoding of local features that are extracted using the Convolutional Neural Network (CNN) model pretrained on ImageNet. Next, to better transfer the...
Cancer diagnosis is one of the emerging applications in microarray gene expression data. Feature selection plays a crucial role because of the huge dimensionality and less training and testing samples. Finding a small subset of significant genes from a large set of gene expression data is a challenging task. This paper presents the usage of genetic algorithm as a tool to determine the informative...
Learning effective and efficient classifiers for imbalanced data is one of ten challenge problems in data mining research. Studying classifiers for imbalanced data is a popular area in machine learning and data mining, which also has great significance in many areas, such as cancer diagnose, credit card fraud detection and intrusion detection. The study for imbalanced data classification can be divided...
Lung cancer is one among the major causes of cancer related deaths. Fortunately, an early stage diagnosis can increase the survival rates of the patients. Sputum cytology is one of the easiest and cost-effective method for lung cancer diagnosis. Chances of misdiagnosis and sampling error related to sputum cytology led to the concept of malignancy associated changes. Malignancy associated changes (MAC)...
Predicting drug response to cancer disease is an important problem in modern clinical oncology that attracted increasing recent attention from various domains such as computational biology, machine learning, and data mining. Cancer patients respond differently to each cancer therapy owing to disease diversity, genetic factors, and environmental causes. Thus, oncologists aim to identify the effective...
This study is carried out on application of different machine learning techniques for prediction of features using bioinformatics data such as that of cancer. The prediction process is undertaken based on feature extraction and then on feature selection process. After selecting the relevant features, machine learning techniques like Support vector machine (SVM), Extreme learning machine (ELM) are...
Feature selection is a key step in classification of high-dimensional data, especially gene expression microarray data with many thousands of features. As a wrapper method, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) is one of the most powerful feature selection techniques. Although SVM-RFE can remove irrelevant features effectively, it cannot deal with most of the redundant features...
The disease Leukemia are continuously increasing among the people. The cause of leukemia is unknown but several factors, however are associated with the development of leukemia that are exposure to ionising radiation, exposure to benzene in rubber industry workers, cytotoxic drug particularly alkylating agent exposure, genetic disorder like down syndrome and immunological deficiency states. There...
In this project, we assessed the clinical value of tumor heterogeneity measured with 18F-FLT as a biomarker for lung cancer diagnosis and staging, then compared its performance to traditional image features using final pathology as gold standard. We also proposed to apply support vector machine (SVM) to train a vector of image features including heterogeneity extracted from PET image and CT texture...
Timely and accurate information about patients' symptoms is important for clinical decision making such as adjustment of medication. Due to the limitations of self-reported symptom such as pain, we investigated whether facial images can be used for detecting pain level accurately using existing algorithms and infrastructure for cancer patients. For low cost and better pain management solution, we...
The number of Circulating Tumor Cells (CTCs) in blood provides an indication of disease progression and tumor response to chemotherapeutic agents. Hence, routine detection and enumeration of CTCs in clinical blood samples have significant applications in early cancer diagnosis and treatment monitoring. In this paper, we investigate two classifiers for image-based CTC detection: (1) Support Vector...
This paper presents the usage of Extreme Learning Machines for cancer microarray gene expression data. Extreme Learning Machines overcomes the problems of overfitting, local minima and improper training rate that are most common in traditional algorithms. We have evaluated the binary classification performance of Extreme Learning Machines on five bench marked datasets of cancer microarray gene expression...
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