Diagnostic Classification of Lung CT Images Using Deep 3D Multi-Scale Convolutional Neural Network

Tafti, Ahmad P.; Bashiri, Fereshteh S.; LaRose, Eric; Peissig, Peggy L.

doi:10.1109/ichi.2018.00078

Cited by 22 publications

(16 citation statements)

References 11 publications

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“…Indeed, even a novel algorithms have likewise proposed dependent on the machine learning techniques like, the creators have planned and built up their own novel algorithms for the usage of lung cancer detection. In [1], the authors Tefti et al and Iyer .An et al have planned and built up the deep learning techniques to improve the accuracy of the lung cancer detection rather than the existing. In [2], Cengil E and Cinar A have proposed the lung cancer distinguishing proof through deep learning for grouping and furthermore it very well may be actualized utilizing the tensorFlow and 3D CNN architecture of deep learning classification and expectation of stages.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Lung Cancer from Pre-processed CT Scan Image using CNN

Goutam¹

2020

IJRASET

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Lately, computerised picture processing is generally utilised for the clinical treatment grouping and conclusion. Lung cancer is the most driving reason for death throughout the world these days. In light of the signs and side effects it can't be finding and treatment arranged at the beginning time. Anyway it very well may be recognised through the side effects like coughing out blood and chest torment, the stages and hazard elements of the malignant growth can't be distinguished through the symptoms. The CT scanned lung pictures ought to be engaged with picture order processing for prior prediction of stages and treatment finding. In existing, machine learning treatment characterisation should be possible through the SVM classification. If there should arise an occurrence of enormous arrangement of images for processing tests, this won't be in precise way and it has less accuracy due to inappropriate component extraction strategies. In this manner the performance of the order depends on the segmented highlights acquired in before areas. The extricated fine-grained training data through deep learning are used for the grouping utilizing Convolution Neural Network (CNN). Right now, propose a novel structure to order both little cell and enormous cell lung cancer and foresee its type and treatment utilizing CNN. It is additionally focuses on the preprocessing and segment procedures to achieve the exactness in expectation. The test results in Python -TensorFlow with Kaggle picture dataset show that contrasted with best in class of arrangement and forecast strategies, the proposed plan can get a lot higher exactness in type expectation and treatment determination.

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Section: Related Workmentioning

confidence: 99%

Prediction of Lung Cancer from Pre-processed CT Scan Image using CNN

Goutam¹

2020

IJRASET

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“…To train the network with "heavy" multimedia, one needs to have large set of input nodes to pass the information through the network. In [24,25], the authors used extremely large Computer-Aided Detection (CAD) 3D images of lung cancer to provide the classification. To achieve this, they used U-Net LUNA 16 labeled data nodules to pass throughout the network.…”

Section: State Of the Art For Medical Imaging Classification Solutionsmentioning

confidence: 99%

“…Our system was trained using these images to be able to classify a new (previously unknown) image into one of the two piles (pile of cancer or pile of cancer-free) and tested the network to determine the success rate. Similar to the authors of [24,25], we divided the image into smaller pieces (using the convolution layer). Unlike the work in [24,25], our algorithm uses the entire image (combined with the pieces) for each following layer, reduced with a max-pooling algorithm.…”

Section: State Of the Art For Medical Imaging Classification Solutionsmentioning

confidence: 99%

“…Similar to the authors of [24,25], we divided the image into smaller pieces (using the convolution layer). Unlike the work in [24,25], our algorithm uses the entire image (combined with the pieces) for each following layer, reduced with a max-pooling algorithm. When the initial success rate of training the network was satisfactory (fit value), the topology was saved and further asynchronously tested against an additional dataset.…”

Section: State Of the Art For Medical Imaging Classification Solutionsmentioning

confidence: 99%

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Using Double Convolution Neural Network for Lung Cancer Stage Detection

Jakimovski

Davčev

2019

Applied Sciences

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Recently, deep learning is used with convolutional Neural Networks for image classification and figure recognition. In our research, we used Computed Tomography (CT) scans to train a double convolutional Deep Neural Network (CDNN) and a regular CDNN. These topologies were tested against lung cancer images to determine the Tx cancer stage in which these topologies can detect the possibility of lung cancer. The first step was to pre-classify the CT images from the initial dataset so that the training of the CDNN could be focused. Next, we built the double Convolution deep Neural Network with max pooling to perform a more thorough search. Finally, we used CT scans of different Tx cancer stages of lung cancer to determine the Tx stage in which the CDNN would detect possibility of lung cancer. We tested the regular CDNN against our double CDNN. Using this algorithm, doctors will have additional help in early lung cancer detection and early treatment. After extensive training with 100 epochs, we obtained the highest accuracy of 0.9962, whereas the regular CDNN obtained only 0.876 accuracy.

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“…Even a novel algorithms have also proposed based on the machine learning techniques like [7] [8], the authors have designed and developed their own novel algorithms for the implementations of the lung cancer detection. In [9] [10], the authors Tefti et al and Iyer .A et al have designed and developed the deep learning techniques to improve the accuracy of the lung cancer detection rather than the existing.…”

Section: Related Workmentioning

confidence: 99%

Classification and Stage Prediction of Lung Cancer using Convolutional Neural Networks

Narmada¹,

Prabakaran²,

Mohan³

2019

IJITEE

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In recent years, digital image processing is widely used for the medical treatment classification and diagnosis. Lung cancer is the most leading cause of death in all over the world nowadays. Based on the signs and symptoms it can’t be diagnosis and treatment classified at the early stage. However it can be identified through the symptoms like coughing up blood and chest pain, the stages and risk factors of the cancer cannot be identified through the symptoms. The CT scanned lung images should be involved in image classification processing for earlier prediction of stages and treatment diagnosis. In existing, machine learning treatment classification can be done through the SVM classification. In case of large set of training samples, this will not be in accurate manner and it has less accuracy because of improper feature extraction techniques. Thus the performance of the classification based on the segmented features obtained in preceding sections. The extracted fine-grained training data through deep learning are utilized for the classification using Convolution Neural Network (CNN). In this paper, we propose a novel framework to classify both small cell and large cell lung cancer and predict its type and treatment using CNN. It is also concentrates on the preprocessing and segmentation processes to accomplish the accuracy in prediction. The experiment results in Python - TensorFlow with Kaggle image dataset show that compared to state of the art of classification and prediction methods, the proposed scheme can obtain much higher accuracy in type prediction and treatment diagnosis.

show abstract

Diagnostic Classification of Lung CT Images Using Deep 3D Multi-Scale Convolutional Neural Network

Cited by 22 publications

References 11 publications

Prediction of Lung Cancer from Pre-processed CT Scan Image using CNN

Prediction of Lung Cancer from Pre-processed CT Scan Image using CNN

Using Double Convolution Neural Network for Lung Cancer Stage Detection

Classification and Stage Prediction of Lung Cancer using Convolutional Neural Networks

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