Lung-Nodule Classification Based on Computed Tomography Using Taxonomic Diversity Indexes and an SVM

Filho, Antonio Oseas Carvalho; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de; Nunes, Rodolfo Acatauassú; Gattass, Marcelo

doi:10.1007/s11265-016-1134-5

Cited by 27 publications

(8 citation statements)

References 26 publications

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“…On a large-scale and high-throughput data mining field, especially medical imaging analysis, machine learning-based statistical analysis techniques are widely used. For example, de Carvalho Filho et al 28 used the support vector machine algorithm for lung nodule classification. Lao et al 12 used the lasso Cox regression model to find a useful subset of reduced features, then constructed radiomics signatures to predict the OS of patients with GBM.…”

Section: Discussionmentioning

confidence: 99%

Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas

Han

Qin

Bay

et al. 2019

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Patient survival in high-grade glioma remains poor, despite the recent developments in cancer treatment. As new chemo-, targeted molecular, and immune therapies emerge and show promising results in clinical trials, image-based methods for early prediction of treatment response are needed. Deep learning models that incorporate radiomics features promise to extract information from brain MR imaging that correlates with response and prognosis. We report initial production of a combined deep learning and radiomics model to predict overall survival in a clinically heterogeneous cohort of patients with high-grade gliomas. MATERIALS AND METHODS: Fifty patients with high-grade gliomas from our hospital and 128 patients with high-grade glioma from The Cancer Genome Atlas were included. For each patient, we calculated 348 hand-crafted radiomics features and 8192 deep features generated by a pretrained convolutional neural network. We then applied feature selection and Elastic Net-Cox modeling to differentiate patients into long-and short-term survivors. RESULTS: In the 50 patients with high-grade gliomas from our institution, the combined feature analysis framework classified the patients into long-and short-term survivor groups with a log-rank test P value , .001. In the 128 patients from The Cancer Genome Atlas, the framework classified patients into long-and short-term survivors with a log-rank test P value of .014. For the mixed cohort of 50 patients from our institution and 58 patients from The Cancer Genome Atlas, it yielded a log-rank test P value of .035. CONCLUSIONS: A deep learning model combining deep and radiomics features can dichotomize patients with high-grade gliomas into long-and short-term survivors. ABBREVIATIONS: C-indices 4 concordance indices; CNN 4 convolutional neural network; GBM 4 glioblastoma multiforme; HGG 4 high-grade glioma; OS 4 overall survival; SE 4 spin-echo; TCGA 4 the Cancer Genome Atlas G lioblastoma multiforme (GBM), the largest diagnostic subcategory of high-grade glioma (HGG) and the most common malignant adult brain tumor, afflicts 12,000-13,000 new patients annually in the United States. GBM, comprising a genetically and phenotypically heterogeneous category of tumors, has a

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Section: Discussionmentioning

confidence: 99%

Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas

Han

Qin

Bay

et al. 2019

AJNR Am J Neuroradiol

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“…Theriot et al [ 9 ] used a logistic model to classify the patients with non-C. difficile diarrhea, C. difficile infection, and the patients who are asymptomatically colonized with C. difficile. Carvalho et al [ 10 ] combined fuzzy logic and a support vector machine (SVM) to diagnose lung nodules; the fuzzy rule was designed by a professional doctor. Similarly, Soundararajan et al [ 11 ] proposed a fuzzy logic-based knowledge system for tuberculosis recognition.…”

Section: Related Workmentioning

confidence: 99%

A Light-Weight Practical Framework for Feces Detection and Trait Recognition

Leng

Yang

Kim

et al. 2020

Sensors

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Fecal trait examinations are critical in the clinical diagnosis of digestive diseases, and they can effectively reveal various aspects regarding the health of the digestive system. An automatic feces detection and trait recognition system based on a visual sensor could greatly alleviate the burden on medical inspectors and overcome many sanitation problems, such as infections. Unfortunately, the lack of digital medical images acquired with camera sensors due to patient privacy has obstructed the development of fecal examinations. In general, the computing power of an automatic fecal diagnosis machine or a mobile computer-aided diagnosis device is not always enough to run a deep network. Thus, a light-weight practical framework is proposed, which consists of three stages: illumination normalization, feces detection, and trait recognition. Illumination normalization effectively suppresses the illumination variances that degrade the recognition accuracy. Neither the shape nor the location is fixed, so shape-based and location-based object detection methods do not work well in this task. Meanwhile, this leads to a difficulty in labeling the images for training convolutional neural networks (CNN) in detection. Our segmentation scheme is free from training and labeling. The feces object is accurately detected with a well-designed threshold-based segmentation scheme on the selected color component to reduce the background disturbance. Finally, the preprocessed images are categorized into five classes with a light-weight shallow CNN, which is suitable for feces trait examinations in real hospital environments. The experiment results from our collected dataset demonstrate that our framework yields a satisfactory accuracy of 98.4%, while requiring low computational complexity and storage.

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“…There are many algorithms like SVM [3,5,7,13,19,27,34], CNN [15,21,31] PSO [7], ANN [17], 2D and 3D CNN that are used to classify the cancerous and non-cancerous nodules [16]. For best classification the combined classifiers of [11].…”

Section: Classification Algorithmsmentioning

confidence: 99%

Lungs Nodule Detection Using Semantic Segmentation and Classification with Optimal Features

Meraj¹,

Hassan²,

Zahoor³

et al. 2019

Preprint

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Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists need support from automated tools for precise opinion. Automated detection of the affected lungs nodule is difficult because of the shape similarity among healthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer. In this article, we propose a framework to precisely detect lungs cancer by classifying it between benign and malignant nodules. The framework is tested using the subset of the publicly available at the Lung Image Database Consortium image collection (LIDC-IDRI). Multiple techniques including filtering and noise removing are applied for pre-processing. Subsequently, the OTSU and the semantic segmentation are used to accurately detect the unhealthy lungs nodules. In total, 13 nodules features were extracted using Principal Components Analysis (PCA) algorithm. Four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are used along with two types of validation schemes i.e. train test holdout validation with 70-30 data split and 10 fold cross-validation. Our experiments show that the proposed system provides 99.23\% accuracy using logic boost classifier.

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Lung-Nodule Classification Based on Computed Tomography Using Taxonomic Diversity Indexes and an SVM

Cited by 27 publications

References 26 publications

Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas

Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas

A Light-Weight Practical Framework for Feces Detection and Trait Recognition

Lungs Nodule Detection Using Semantic Segmentation and Classification with Optimal Features

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