Pulmonary nodule detection on computed tomography using neuro-evolutionary scheme

Huidrom, Ratishchandra; Chanu, Yambem Jina; Singh, Kh. Manglem

doi:10.1007/s11760-018-1327-4

Cited by 20 publications

(9 citation statements)

References 22 publications

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“…Radiomic texture features have been widely used in tissue characterization for discriminating different types of lesions in thoracic CT images, such as automatic lung nodule detection [28][29][30][31][32][33][34][35][36], differential diagnosis of benign and malignant lung nodules [15,16], and differentiation of lung cancer subtypes [17]. Several texture features have been suggested for automated detection of pulmonary nodules in CT images, including the mean, skewness, and kurtosis values of intensity histograms [28][29][30][31][32][33], local binary patterns [9,14,34,35], and gray-level co-occurrence matrix (GLCM)-based features [28][29][30]36]. While radiomic texture features were shown to be effective in distinguishing various types of lung nodules, discrimination between SPCH and LPA was intrinsically di cult due to their common GGN-like appearance.…”

Section: Discussionmentioning

confidence: 99%

Radiomic Feature Analysis for Distinguishing Solitary Pulmonary Capillary Hemangioma From Lepidic-Predominant Lung Adenocarcinoma in Ground Glass Lung Nodules

Lin¹,

Wang²,

Chen³

et al. 2020

Preprint

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Solitary pulmonary capillary hemangioma (SPCH) is a benign lung tumor that presents as ground-glass nodules (GGN) on computed tomography (CT) images, mimicking lepidic-predominant adenocarcinoma (LPA). This study aimed to establish a discriminant model using a radiomic feature analysis to distinguish SPCH from LPA in lung GGNs. This study included 13 and 49 patients who underwent complete resection for lung SPCH and LPA, respectively. An SPCH/LPA classification model was proposed based on a two-level decision tree and 26 radiomic features extracted from each segmented lesion, including 5 and 21 features from the histogram and co-occurrence matrix, respectively. The two-level decision tree was constructed based on the training data with a support vector machine (SVM) as the classifier in each tree node. For comparison, a baseline model was built with the same 26 features using an SVM as the classifier. Both models were assessed by the leave-one-out cross-validation method. The area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of the proposed SPCH/LPA model were 0.954, 91.9%, 92.3%, and 91.8%. The proposed SPCH/LPA model significantly outperformed the baseline model (p<0.05). Our results may help surgeons to preoperatively discriminate SPCH from LPA, thus avoiding unnecessary surgery for benign tumors.

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Section: Discussionmentioning

confidence: 99%

Radiomic Feature Analysis for Distinguishing Solitary Pulmonary Capillary Hemangioma From Lepidic-Predominant Lung Adenocarcinoma in Ground Glass Lung Nodules

Lin¹,

Wang²,

Chen³

et al. 2020

Preprint

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“…[166][167][168] showed algorithms that achieved better results that year. The deep learning methods [71,[169][170][171][172] for lung nodule detection did not show promising results.…”

Section: Lidc-idrimentioning

confidence: 96%

“…Armato et al [161] believed that better results can be obtained by combining geometric texture with the directional gradient histogram with reduced HOG-PCA features to create a hybrid feature vector for each candidate node. Huidrom et al [162] used a nonlinear algorithm to classify the 3D nodule candidate boxes. The proposed algorithm is based on the combination of genetic algorithm (GA) and the particle swarm optimization (PSO) to prove the learning ability of multi-layer perceptron.…”

Section: Lidc-idrimentioning

confidence: 99%

Survey on deep learning for pulmonary medical imaging

Song

Tian

et al. 2019

Front. Med.

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As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and diagnosis, deep learning-based approaches have emerged as powerful techniques in medical image areas. In this process, feature representations are learned directly and automatically from data, leading to remarkable breakthroughs in the medical field. Deep learning has been widely applied in medical imaging for improved image analysis. This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes. The topics include classification, detection, and segmentation tasks on medical image analysis with respect to pulmonary medical images, datasets, and benchmarks. A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases, pulmonary embolism, pneumonia, and interstitial lung disease is also provided. Lastly, the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

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“…However, algorithms based on shape (23) and template matching (24), as well as morphological approaches with convexity models (25) and filtering-based methods (26), are also capable of successfully detecting candidate nodules with high accuracy. In 2019, a polygonal approximation algorithm (27) was proposed, followed by a neuro-evolutionary scheme (28) in 2020. Since 2016, deep learning networks have played an important role in 3 summarizes such algorithms together with their reported accuracy.…”

Section: Computer-aided Detection Systems For Detection and Diagnosis Of Pulmonary Nodulesmentioning

confidence: 99%

Radiomics and artificial intelligence in lung cancer screening

Bińczyk¹,

Prazuch²,

Bozek³

et al. 2021

Transl Lung Cancer Res

101

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Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76 million associated deaths reported in 2018. The key issue in the fight against this disease is the detection and diagnosis of all pulmonary nodules at an early stage. Artificial intelligence (AI) algorithms play a vital role in the automated detection, segmentation, and computer-aided diagnosis of malignant lesions. Among the existing algorithms, radiomics and deep-learning-based types appear to show the most promise. Radiomics is a growing field related to the extraction of a set of features from an image, which allows for automated classification of medical images into a predefined group. The process comprises a series of consecutive steps including image acquisition and pre-processing, segmentation of the desired region of interest, calculation of defined features, feature engineering, and construction of the classification model. The features calculated in this process are mainly shape features, as well as first-and higher-order texture features. To date, more than 100 features have been defined, although this number varies depending on the application. The greatest challenge in radiomics is building a cross-validated model based on a selected set of calculated features known as the radiomic signature. Numerous radiomic signatures have successfully been developed; however, reproducibility and clinical validity of the results obtained constitutes a considerable challenge of modern radiomics. Deep learning algorithms are another rapidly evolving technique and are recognized as a valuable tool in the field of medical image analysis for the detection, characterization, and assessment of lesions.Such an approach involves the design of artificial neural network architecture while upholding the goal of high classification accuracy. This paper illuminates the evolution and current state of artificial intelligence methods in lung imaging and the detection and diagnosis of pulmonary nodules, with a particular emphasis on radiomics and deep learning methods.

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Pulmonary nodule detection on computed tomography using neuro-evolutionary scheme

Cited by 20 publications

References 22 publications

Radiomic Feature Analysis for Distinguishing Solitary Pulmonary Capillary Hemangioma From Lepidic-Predominant Lung Adenocarcinoma in Ground Glass Lung Nodules

Radiomic Feature Analysis for Distinguishing Solitary Pulmonary Capillary Hemangioma From Lepidic-Predominant Lung Adenocarcinoma in Ground Glass Lung Nodules

Survey on deep learning for pulmonary medical imaging

Radiomics and artificial intelligence in lung cancer screening

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