Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier

Kasinathan, Gopi; Jayakumar, S.; Gandomi, Amir H.; Ramachandran, Manikandan; Fong, Simon; Patan, Rizwan

doi:10.1016/j.eswa.2019.05.041

Cited by 88 publications

(38 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…CNNs are highly accurate in object detection and segmentation, such as pedestrian detection, 20 action detection, 21 object detection, 22 and saliency detection. 23 Current research focuses on natural images, and research in medicine includes tumor detection 24 and tumor segmentation. 25 All of these examples show that CNNs are excellent algorithms in image recognition and segmentation.…”

Section: Introductionmentioning

confidence: 99%

Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network

Feng

Zhao

et al. 2020

J Applied Clin Med Phys

View full text Add to dashboard Cite

Purpose Focal cortical dysplasia (FCD) is a common cause of epilepsy; the only treatment is surgery. Therefore, detecting FCD using noninvasive imaging technology can help doctors determine whether surgical intervention is required. Since FCD lesions are small and not obvious, diagnosing FCD through visual evaluations of magnetic resonance imaging (MRI) scans is difficult. The purpose of this study is to detect and segment histologically confirmed FCD lesions in images of normal fluid‐attenuated inversion recovery (FLAIR)‐negative lesions using convolutional neural network (CNN) technology. Methods The technique involves training a six‐layer CNN named Net‐Pos, which consists of two convolutional layers (CLs); two pooling layers (PLs); and two fully connected (FC) layers, including 60 943 learning parameters. We employed activation maximization (AM) to optimize a series of pattern image blocks (PIBs) that were most similar to a lesion image block by using the trained Net‐Pos. We developed an AM and convolutional localization (AMCL) algorithm that employs the mean PIBs combined with convolution to locate and segment FCD lesions in FLAIR‐negative patients. Five evaluation indices, namely, recall, specificity, accuracy, precision, and the Dice coefficient, were applied to evaluate the localization and segmentation performance of the algorithm. Results The PIBs most similar to an FCD lesion image block were identified by the trained Net‐Pos as image blocks with brighter central areas and darker surrounding image blocks. The technique was evaluated using 18 FLAIR‐negative lesion images from 12 patients. The subject‐wise recall of the AMCL algorithm was 83.33% (15/18). The Dice coefficient for the segmentation performance was 52.68. Conclusion We developed a novel algorithm referred to as the AMCL algorithm with mean PIBs to effectively and automatically detect and segment FLAIR‐negative FCD lesions. This work is the first study to apply a CNN‐based model to detect and segment FCD lesions in images of FLAIR‐negative lesions.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network

Feng

Zhao

et al. 2020

J Applied Clin Med Phys

View full text Add to dashboard Cite

show abstract

“…A variety of CNN models, including R-CNN [7], R-FCN [10], hierarchical semantic CNN [25], and Mask R-CNN [26], enable the generation of nodule candidate regions with higher precision and less time than traditional approaches. CNNs also can be used as classifiers to reduce the false positives [23].…”

Section: Related Workmentioning

confidence: 99%

Pulmonary Nodule Detection Using V-Net and High-Level Descriptor Based SVM Classifier

Tian

Liu

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Early detection of the pulmonary nodule is critical to increase the five-year survival rate of lung cancer. Many computer-aided diagnosis (CAD) systems have been proposed for nodule detection to assist radiologists in diagnosis. Along this direction, this paper proposes a novel automated pulmonary nodule detection model using the modified V-Nets and a high-level descriptor based support vector machine (SVM) classifier. The former is for nodule candidate detection and the latter is for false positive (FP) reduction. A hard mining scheme for retraining is devised to improve the FP reduction performance. The proposed SVM classifier, which employs more critical features of CT images, performs superior in FP reduction than other SVM based classifiers and CNN classifiers. Experimental results using the LIDC-IDRI dataset are presented to demonstrate the effectiveness of the proposed CAD model. INDEX TERMS Convolutional neural networks (CNNs), pulmonary nodule detection, lung CT image, classification, support vector machine (SVM)

show abstract

“…Softmax is the activation function used for multi classification and it is used to determine the probability of multiple classes at once and softmax is the last layer of neural network so it is important that number of node in softmax should be same as output layer. G. Kasinathan, S. Jayakumar and A.H. Gandomi et al, [13] here gradient value is calculated with respect to the parameters of CNN model that are used for gradient based optimization. And author has also proposed Enhanced CNN with AlexNet.…”

Section: Use Of Neural Network In Lung Cancer Predictionmentioning

confidence: 99%

Machine Learning Techniques for Prediction of Lung Cancer

Banerjee¹,

Das²

2020

IJRTE

View full text Add to dashboard Cite

Lung cancer has been one of the deadliest diseases in today’s decades. It has become one of the causes of death in both man and woman. There are various reasons for which lung cancer occurs but classification of tumor and predicting it in the right stage is the most important part. This paper focused on the numerous approaches has been derived for lung cancer detection from different literature survey to advance the ability of detection of cancer. Digital image processing and data mining both are equally important because for prediction either image dataset or statistical dataset is used so for pre-processing the image dataset digital image processing is applied for statistical dataset data mining is applied. After pre-processing, segmentation and feature extraction we apply various machine learning algorithm for the prediction of lung cancer. So first we have provided a sketch of Machine learning and then various fields like in image data or statistical data where machine learning has been used for classification. Once the classification is done confusion matrix is generated for calculating accuracy, sensitivity, precision, these method is used to measure the rate of accuracy of the proposed model.

show abstract

Automated 3-D lung tumor detection and classification by an active contour model and CNN classifier

Cited by 88 publications

References 14 publications

Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network

Automatic localization and segmentation of focal cortical dysplasia in FLAIR‐negative patients using a convolutional neural network

Pulmonary Nodule Detection Using V-Net and High-Level Descriptor Based SVM Classifier

Machine Learning Techniques for Prediction of Lung Cancer

Contact Info

Product

Resources

About