Convolutional neural networks for lung cancer screening in computed tomography (CT) scans

Rao, Prajwal; Fereira, Nishal Ancelette; Srinivasan, Raghuram

doi:10.1109/ic3i.2016.7918014

Cited by 44 publications

(18 citation statements)

References 8 publications

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“…In comparison with traditional Artificial Neural Networks (ANN) and LeNet, their proposed CanNet model achieved the highest accuracy rate in the classification of lung CT scan images. The accuracy rate of each LeNet, ANN and CanNet was 56%, 72.5% and 76% respectively [15]. Song .…”

Section: Related Workmentioning

confidence: 98%

Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture

2019

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Lung cancer is the most common cause of cancer-related deaths worldwide. Hence, the survival rate of patients can be increased by early diagnosis. Recently, machine learning methods on Computed Tomography (CT) images have been used in the diagnosis of lung cancer to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on CT images are complicated processes. Hence, deep learning as an effective area of machine learning methods by using automatic feature extraction methods could minimize the process of feature extraction. In this study, two Convolutional Neural Network (CNN)-based models were proposed as deep learning methods to diagnose lung cancer on lung CT images. To investigate the performance of the two proposed models (Straight 3D-CNN with conventional softmax and hybrid 3D-CNN with Radial Basis Function (RBF)-based SVM), the altered models of two-well known CNN architectures (3D-AlexNet and 3D-GoogleNet) were considered. Experimental results showed that the performance of the two proposed models surpassed 3D-AlexNet and 3D-GoogleNet. Furthermore, the proposed hybrid 3D-CNN with SVM achieved more satisfying results (91.81%, 88.53% and 91.91% for accuracy rate, sensitivity and precision respectively) compared to straight 3D-CNN with softmax in the diagnosis of lung cancer.

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Section: Related Workmentioning

confidence: 98%

Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture

2019

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“…In Reference 13, Prajwal Rao foreseen interpretation invariance of CNNs is misused to characterize the lung malignant growth screening thoracic CT examines productively. By utilizing CNNs, one can renounce the dull procedure of physically extracting features for classification which requires particular domain learning.…”

Section: Related Workmentioning

confidence: 99%

Morphological feature extraction and KNG‐CNN classification of CT images for early lung cancer detection

Jena

George

2020

Int J Imaging Syst Tech

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Lung cancer is a dangerous disease causing death to individuals. Currently precise classification and differential diagnosis of lung cancer is essential with the stability and accuracy of cancer identification is challenging. Classification scheme was developed for lung cancer in CT images by Kernel based Non‐Gaussian Convolutional Neural Network (KNG‐CNN). KNG‐CNN comprises of three convolutional, two fully connected and three pooling layers. Kernel based Non‐Gaussian computation is used for the diagnosis of false positive or error encountered in the work. Initially Lung Image Database Consortium image collection (LIDC‐IDRI) dataset is used for input images and a ROI based segmentation using efficient CLAHE technique is carried as preprocessing steps, enhancing images for better feature extraction. Morphological features are extracted after the segmentation process. Finally, KNG‐CNN method is used for effectual classification of tumour > 30mm. An accuracy of 87.3% was obtained using this technique. This method is effectual for classifying the lung cancer from the CT scanned image.

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“…Preprocessing was done by median filter and segmentation is done by thresholding method and histogram equalization then texture features are extracted. Rao et al, (2016) presented a convolutional neural network for lung cancer screening in CT scans. They proposed a CNN technique for the analysis of CT scans with tumors.…”

Section: Related Workmentioning

confidence: 99%

“…They can utilize those datasets for training and testing of their algorithms and models. Among those databases, most commonly used are LIDC, LIDC-IDRI and ANODE09 (Armato III et al, 2011;Cascio et al, 2012;Keshani et al, 2013;Biradar and Agalatakatti, 2015;Shyamala and Pushparani, 2016;Rao et al, 2016).…”

Section: Data Acquisitionmentioning

confidence: 99%

Detection of Pulmonary Nodules in ct Images Using Deep Learning Technique

Balachandran¹,

Divya²,

Rajendran³

et al. 2020

Journal of Computer Science

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Lung Cancer is one of the most deadly diseases worldwide. According to the American Cancer Society, about 234,030 peoples have been suffering from lung cancer. It can be cured if it is diagnosed earlier which decreases the death rate. A computational diagnostic tool named Computer Aided Diagnosis (CAD) is used to detect pulmonary nodules. Extensive work has been made in this domain. However, previous Computer Aided Diagnosis (CAD) system are time-consuming since they needed more modules such as image modification, segmentation and the features should be extracted by the domain experts to build the entire CAD system. It is hard to examine large data using the existing CAD system. Thus, a novel framework with a Convolutional Neural Network (CNN) to detect pulmonary nodule is proposed. Firstly, a preprocessing technique named bilateral filtering is applied to increase the image quality and remove the irrelevant noise from the Computer Tomography (CT) images. Secondly, the preprocessed data are trained into a convolutional neural network to detect the nodule and classify it. The performance of this system is validated using the Lung Image Database Consortium (LIDC) dataset. The accuracy of nodule candidate detection achieves 93%. It states that the proposed method achieves better accuracy in nodule detection.

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Convolutional neural networks for lung cancer screening in computed tomography (CT) scans

Cited by 44 publications

References 8 publications

Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture

Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture

Morphological feature extraction and KNG‐CNN classification of CT images for early lung cancer detection

Detection of Pulmonary Nodules in ct Images Using Deep Learning Technique

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