Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)
Reads0
Chats0
TLDR
This paper demonstrates a computer-aided diagnosis (CAD) system for lung cancer classification of CT scans with unmarked nodules, a dataset from the Kaggle Data Science Bowl, 2017, which outperforms the current CAD systems in literature.Abstract:
This paper demonstrates a computer-aided diagnosis
(CAD) system for lung cancer classification of CT scans
with unmarked nodules, a dataset from the Kaggle Data Science
Bowl, 2017. Thresholding was used as an initial segmentation
approach to segment out lung tissue from the rest of the CT
scan. Thresholding produced the next best lung segmentation.
The initial approach was to directly feed the segmented CT
scans into 3D CNNs for classification, but this proved to be
inadequate. Instead, a modified U-Net trained on LUNA16 data
(CT scans with labeled nodules) was used to first detect nodule
candidates in the Kaggle CT scans. The U-Net nodule detection
produced many false positives, so regions of CTs with segmented
lungs where the most likely nodule candidates were located as
determined by the U-Net output were fed into 3D Convolutional
Neural Networks (CNNs) to ultimately classify the CT scan as
positive or negative for lung cancer. The 3D CNNs produced
a test set Accuracy of 86.6%. The performance of our CAD
system outperforms the current CAD systems in literature which
have several training and testing phases that each requires
a lot of labeled data, while our CAD system has only three
major phases (segmentation, nodule candidate detection, and
malignancy classification), allowing more efficient training and
detection and more generalizability to other cancers.read more
Citations
More filters
Journal ArticleDOI
Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges
TL;DR: A critical appraisal of popular methods that have employed deep learning techniques for medical image segmentation is presented and the most common challenges incurred are summarized and suggest possible solutions.
Journal ArticleDOI
Deep learning for lung Cancer detection and classification
Asuntha A,Andy Srinivasan +1 more
TL;DR: This work uses best feature extraction techniques such as Histogram of oriented Gradients, wavelet transform-based features, Local Binary Pattern, Scale Invariant Feature Transform, SIFT and Zernike Moment to detect the cancerous lung nodules from the given input lung image and to classify the lung cancer and its severity.
Journal ArticleDOI
Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture
Huseyin Polat,Homay Danaei Mehr +1 more
TL;DR: In this study, two Convolutional Neural Network (CNN)-based models were proposed as deep learning methods to diagnose lung cancer on lung CT images and showed that the performance of the two proposed models surpassed 3D-AlexNet and3D-GoogleNet.
Proceedings ArticleDOI
Challenges and Recent Solutions for Image Segmentation in the Era of Deep Learning
TL;DR: The challenges that are mostly related to architecture and training of deep neural networks are explained and the state-of-the-art solutions applied in the literature are presented to help researchers to design proper network architectures according to their problems.
Journal ArticleDOI
Lung cancer identification: a review on detection and classification.
TL;DR: Different promising approaches developed in the computer-aided diagnosis (CAD) system to detect and classify the nodule through the analysis of CT images to provide radiologists’ assistance are reviewed and the comprehensive analysis of different methods are presented.
References
More filters
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Posted Content
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Book ChapterDOI
Large-Scale Machine Learning with Stochastic Gradient Descent
TL;DR: A more precise analysis uncovers qualitatively different tradeoffs for the case of small-scale and large-scale learning problems.
Proceedings Article
On the importance of initialization and momentum in deep learning
TL;DR: It is shown that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs to levels of performance that were previously achievable only with Hessian-Free optimization.
Proceedings ArticleDOI
Convolutional networks and applications in vision
TL;DR: New unsupervised learning algorithms, and new non-linear stages that allow ConvNets to be trained with very few labeled samples are described, including one for visual object recognition and vision navigation for off-road mobile robots.
Related Papers (5)
The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.
Samuel G. Armato,Geoffrey McLennan,Luc Bidaut,Michael F. McNitt-Gray,Charles R. Meyer,Anthony P. Reeves,Binsheng Zhao,Denise R. Aberle,Claudia I. Henschke,Eric A. Hoffman,Ella A. Kazerooni,Heber MacMahon,Edwin J. R. van Beek,David F. Yankelevitz,Alberto Biancardi,Peyton H. Bland,Matthew S. Brown,Roger Engelmann,Gary E. Laderach,Daniel Max,Richard C. Pais,David Qing,Rachael Y. Roberts,Amanda R. Smith,Adam Starkey,Poonam Batra,Philip Caligiuri,Ali Farooqi,Gregory W. Gladish,C. Matilda Jude,Reginald F. Munden,Iva Petkovska,Leslie E. Quint,Lawrence H. Schwartz,Baskaran Sundaram,Lori E. Dodd,Charles Fenimore,David Gur,Nicholas Petrick,John Freymann,Justin Kirby,Brian Hughes,Alessi Vande Casteele,Sangeeta Gupte,Maha Sallam,Michael D. Heath,Michael Kuhn,Ekta Dharaiya,Richard Burns,David Fryd,Marcos Salganicoff,Vikram Anand,Uri Shreter,Stephen Vastagh,Barbara Y. Croft,Laurence P. Clarke +55 more