Convolutional neural network-based PSO for lung nodule false positive reduction on CT images

Nazir

Microscopy Res & Technique

et al. 2019

The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponential increase in the size of computed tomography (CT) and magnetic resonance imaging (MRI) data. The size of a single CT/MRI image has increased manifold since the inception of these imagery techniques. This demand for the introduction of effective and efficient frameworks for extracting relevant and most suitable information (features) from these sizeable images. As early detection of lungs cancer can significantly increase the chances of survival of a lung scanner patient, an effective and efficient nodule detection system can play a vital role. In this article, we have proposed a novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information. The proposed framework comprises multiple phases that include image contrast enhancement, segmentation, feature extraction, followed by an employment of these features for training and testing of a selected classifier. Image preprocessing and feature selection being the primary steps—playing their vital role in achieving improved classification accuracy. We have empirically tested the efficacy of our technique by utilizing the well‐known Lungs Image Consortium Database dataset. The results prove that the technique is highly effective for reducing FPRs with an impressive sensitivity rate of 97.45%.

Section: Resultsmentioning

confidence: 99%

Lungs nodule detection framework from computed tomography images using support vector machine

Nazir

Microscopy Res & Technique

et al. 2019

“…With the increasing improvement of CAD systems, the majority of studies have demonstrated that CAD systems could detect more nodules than radiologists, even after double reading . Moreover, in comparison with most CAD systems based on supervised machine learning algorithms, multiple studies have shown that deep learning‐based CAD systems (DL‐CAD) have superior detection rates and further reduce false positive rates . However, CAD systems are far from perfect and thus require further development to be improved.…”

Section: Introductionmentioning

confidence: 99%

“…16,17 Moreover, in comparison with most CAD systems based on supervised machine learning algorithms, 18,19 multiple studies have shown that deep learning-based CAD systems (DL-CAD) have superior detection rates and further reduce false positive rates. [20][21][22] However, CAD systems are far from perfect and thus require further development to be improved.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the performance of a deep learning‐based computer‐aided diagnosis (DL‐CAD) system for detecting and characterizing lung nodules: Comparison with the performance of double reading by radiologists

Liu

Huang

et al. 2018

Thoracic Cancer

BackgroundThe study was conducted to evaluate the performance of a state‐of‐the‐art commercial deep learning‐based computer‐aided diagnosis (DL‐CAD) system for detecting and characterizing pulmonary nodules.MethodsPulmonary nodules in 346 healthy subjects (male: female = 221:125, mean age 51 years) from a lung cancer screening program conducted from March to November 2017 were screened using a DL‐CAD system and double reading independently, and their performance in nodule detection and characterization were evaluated. An expert panel combined the results of the DL‐CAD system and double reading as the reference standard.ResultsThe DL‐CAD system showed a higher detection rate than double reading, regardless of nodule size (86.2% vs. 79.2%; P < 0.001): nodules ≥ 5 mm (96.5% vs. 88.0%; P = 0.008); nodules < 5 mm (84.3% vs. 77.5%; P < 0.001). However, the false positive rate (per computed tomography scan) of the DL‐CAD system (1.53, 529/346) was considerably higher than that of double reading (0.13, 44/346; P < 0.001). Regarding nodule characterization, the sensitivity and specificity of the DL‐CAD system for distinguishing solid nodules > 5 mm (90.3% and 100.0%, respectively) and ground‐glass nodules (100.0% and 96.1%, respectively) were close to that of double reading, but dropped to 55.5% and 93%, respectively, when discriminating part solid nodules.ConclusionOur DL‐CAD system detected significantly more nodules than double reading. In the future, false positive findings should be further reduced and characterization accuracy improved.

International Journal of Radiation Oncology*Biology*Physics

“…80,81 Indeed, prognostic biomarkers developed using these machine learning methods have increased performance when compared with conventional statistical methods. 78,82,83 Recently deep learning algorithms such as CNNs have achieved breakthrough prediction power in a variety of medical studies, including detection of lung nodules on CT scans [84][85][86] and detection of breast cancer on mammograms. 87,88 A comparison in mortality prediction from chest CT between a deep learning framework and a standard framework with radiomics features showed increased accuracy with CNN-based classification.…”

Section: Outcome Modeling By Machine Learningmentioning

confidence: 99%

“…89 Multitask learning is expected to help provide a degree of interpretation for deep learning approaches. 76,[81][82][83][84][85][86][87][88][89][90] Given enough high-quality data (text and images), it is expected that the role of CNNs will continue to expand in medicine and quantitative imaging. Despite these advances, however, concerted efforts are needed to promote detailed understanding of these approaches, including the relationship between dataset sizes, possible confounders, and performance of outcome prediction.…”

Section: Outcome Modeling By Machine Learningmentioning

confidence: 99%

NCTN Assessment on Current Applications of Radiomics in Oncology

Nie

Al‐Hallaq

et al. 2019

Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In NotedAn online CME test for this article can be taken at https:// academy.astro.org.