Automatic nodule detection for lung cancer in CT images: A review

Zhang, Guobin; Jiang, Shan; Yang, Zhiyong; Gong, Li; Ma, Xiaodong; Zhou, Zeyang; Bao, Chao; Liu, Qi

doi:10.1016/j.compbiomed.2018.10.033

Cited by 117 publications

(67 citation statements)

References 89 publications

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“…The investigational outcomes related to quantitative scrutiny and ethereal curves demonstrated that the treatment of ALK affected lung tumor implemented with low concentrated medicines would be developed towards the ALK non-affected lung tumor. Guobin Zhang, et.al presented a serious evaluation of the CADe scheme for automated lung cancer recognition with the help of CT descriptions for summarizing the existing developments [10]. In the initial stage, a brief description of CADe scheme was provided.…”

Section: Literature Reviewmentioning

confidence: 99%

Lung Cancer Detection using Convolutional Neural Network

Sharma¹,

Kaur²,

Saini³

2019

IJEAT

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 Abstract: The mortality rate is increasing among the growing population and one of the leading causes is lung cancer. Early diagnosis is required to decrease the number of deaths and increase the survival rate of lung cancer patients. With the advancements in the medical field and its technologies CAD system has played a significant role to detect the early symptoms in the patients which cannot be carried out manually without any error in it. CAD is detection system which has combined the machine learning algorithms with image processing using computer vision. In this research a novel approach to CAD system is presented to detect lung cancer using image processing techniques and classifying the detected nodules by CNN approach. The proposed method has taken CT scan image as input image and different image processing techniques such as histogram equalization, segmentation, morphological operations and feature extraction have been performed on it. A CNN based classifier is trained to classify the nodules as cancerous or non-cancerous.The performance of the system is evaluated in the terms of sensitivity, specificity and accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Lung Cancer Detection using Convolutional Neural Network

Sharma¹,

Kaur²,

Saini³

2019

IJEAT

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“…Multi-class pixel-wise segmentation using deep neural networks, such as SegNet [17] and CNN [18], are examples of deep-learning-based segmentation schemes. Due to the use of multiple network layers, these segmentations are in favor of unique feature learning [19]. However, these methods usually consume a massive amount of data and training efforts.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest

et al. 2020

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The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam.

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“…Initially, the AI is mostly utilized in many monotonous, repetitive tasks and heavy workloads, such as evaluating screening mammography and chest X-rays. (4) At present, with the optimization of computer algorithms and the development of deep convolution neural networks (DCNN), AI has applied to more advanced projects about radiodiagnoses, such as automatic nodule detection for lung cancer in CT images and thyroid cancer identi cation in sonographic images (5,6). However, there were few efforts have been made in nuclear image interpretation, which also requires a variety of repetitive information as well as proper clinical feature extraction for diagnosis of disease, such as thyroid scintigraphy.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolution Neural Network Based Artificial Intelligence Improves Diagnosis of Thyroid Scintigraphy for Thyrotoxicosis: a Dual Center Study

Yang¹,

Pi²,

He³

et al. 2020

Preprint

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Background: 99mTc-pertechnetate thyroid scintigraphy is a valid avenue for distinguishing causes of thyrotoxicosis in the clinic, but the interpretation of thyroid scintigraphic images is subjected with significant variation among different inter-observers. We aim to develop an artificial intelligence (AI) system to improve the diagnosis of thyrotoxicosis.Materials and methods: We constructed an AI model based on a deep neural network with 2468 thyroid scintigraphic images collected from West China Hospital, and evaluated the diagnostic accuracy for classifying four patterns of thyrotoxicosis: ‘diffusely increased,’ ‘diffusely decreased,’ ‘focal increased,’ and ‘heterogeneous uptake.’ Then, we compared the diagnostic performance of the AI model and five physicians with 200 testing cohorts from two centers.Results: We constructed the AI model, which has the best performance in internal database validation based on four kinds of standout pre-trained networks. This AI model achieves satisfactory performance in classifying four patterns of thyrotoxicosis with an overall accuracy of 91.92% for internal and 86.75% for external data validation. In the following contrastive study, the AI model represented improved diagnostic accuracy and consistency than 5 physicians for interpreting data from West China Hospital (88% vs. 66~73%) and Panzhihua Central Hospital (83% vs. 53%~79%), respectively.Conclusion: Deep convolution neural network based AI model represented considerable performance in classifying four patterns of thyroid scintigraphic images; this may help physicians diagnose causes of thyrotoxicosis and reduced the physicians’ error rate.

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Automatic nodule detection for lung cancer in CT images: A review

Cited by 117 publications

References 89 publications

Lung Cancer Detection using Convolutional Neural Network

Lung Cancer Detection using Convolutional Neural Network

Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest

Deep Convolution Neural Network Based Artificial Intelligence Improves Diagnosis of Thyroid Scintigraphy for Thyrotoxicosis: a Dual Center Study

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