Computer aided lung cancer diagnosis with deep learning algorithms

A survey on deep learning in medical image analysis

“…As summarized in Table 2, most works employ simple Anavi et al (2015) Image retrieval Combines classical features with those from pre-trained CNN for image retrieval using SVM Bar et al (2015) Pathology detection Features from a pre-trained CNN and low level features are used to detect various diseases Anavi et al (2016) Image retrieval Continuation of Anavi et al (2015), adding age and gender as features Bar et al (2016) Pathology detection Continuation of Bar et al (2015), more experiments and adding feature selection Cicero et al (2016) Pathology detection GoogLeNet CNN detects five common abnormalities, trained and validated on a large data set Tuberculosis detection Processes entire radiographs with a pre-trained fine-tuned network with 6 convolution layers Kim and Hwang (2016) Tuberculosis detection MIL framework produces heat map of suspicious regions via deconvolution Shin et al (2016a) Pathology detection CNN detects 17 diseases, large data set (7k images), recurrent networks produce short captions Rajkomar et al (2017) Frontal/lateral classification Pre-trained CNN performs frontal/lateral classification task Yang et al (2016c) Bone suppression Cascade of CNNs at increasing resolution learns bone images from gradients of radiographs Wang et al (2016a) Nodule classification Combines classical features with CNN features from pre-trained ImageNet CNN Used a standard feature extractor and a pre-trained CNN to classify detected lesions as benign peri-fissural nodules van Detects nodules with pre-trained CNN features from orthogonal patches around candidate, classified with SVM Shen et al (2015b) Three CNNs at different scales estimate nodule malignancy scores of radiologists (LIDC-IDRI data set) Chen et al (2016e) Combines features from CNN, SDAE and classical features to characterize nodules from LIDC-IDRI data set Ciompi et al (2016) Multi-stream CNN to classify nodules into subtypes: solid, part-solid, non-solid, calcified, spiculated, perifissural Dou et al (2016b) Uses 3D CNN around nodule candidates; ranks #1 in LUNA16 nodule detection challenge Li et al (2016a) Detects nodules with 2D CNN that processes small patches around a nodule Setio et al (2016) Detects nodules with end-to-end trained multi-stream CNN with 9 patches per candidate Shen et al (2016) 3D CNN classifies volume centered on nodule as benign/malignant, results are combined to patient level prediction Sun et al (2016b) Same dataset as Shen et al (2015b)…”

Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)

“…In [10], Suna W. et al, implemented three different deep learning algorithms, Convolutional Neural Network (CNN), Deep Belief Networks (DBNs), Stacked Denoising Autoencoder (SDAE), and compared them with the traditional image feature based CAD system. The CNN architecture contains eight layers of convolutional and pooling layers, interchangeably.…”

“…However, this is much less efficient than basic thresholding, so due to time limitations, it was not possible to preprocess all CT scans using Watershed, so thresholding is used instead. [10]. U-Net is a 2D CNN architecture that is popular for biomedical image segmentation.…”

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“…NIH/NCI Lung Image Database Consortium (LIDC) public database is used in Refs. [6][7][8][9][10][11][12][13][14] to access a large number of CT Scan images. In addition to the public databases, Early Lung Cancer Action Program (ELCAP), the National Biomedical Imaging Archive (NBIA) [15][16][17], National Lung Screening Trial (NLST) [18], King Hussein Cancer Center [19], Apollo Specialty Hospitals, Chennai [20], Cornell University database [21], University of Michigan (Dept.…”

“…Binarization is used to extract multiple features [16,17,20,27,30,31,39,47]. Several texture features proved to be useful a feature, including uniformity, entropy, maximum probability, inertia, inverse difference, correlation, homogeneity, dissimilarity, autocorrelation, cluster shade, cluster prominence, inverse difference normalized, sum entropy, sum average, sum of squares, sum variance, difference variance, difference entropy, information measures of correlation and maximal correlation coefficient extracted from gray level co-occurrence matrix (GLCM) [13,16,20,[30][31][32]39,47], sequential forward selection [28,31], spatial gray level dependence matrix (SGLDM) [40], genetic algorithm [32], masking approach [17,27], histogram [16,40], PCA [22,49], region growing technique [24,45], linear discriminant analysis (LDA) [15,45], filter bank method [28], box-counting method [40], contrast enhancement and calcification [34] and gray-weighted distance transformation [41] are used for feature extraction with high discrimination ability for classification.…”