An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets

Zhang, Shu; Han, Fangfang; Zhang, Liang; Tan, James S.; Cao, Weiguo; Gao, Yongfeng; Pomeroy, Marc J.; Ng, Kenneth; Hou, Wei

doi:10.1016/j.compmedimag.2019.101645

Cited by 38 publications

(24 citation statements)

References 40 publications

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“…While there are several texture descriptors available for this exploratory study, we choose the Haralick texture descriptor 31 based on our experience in adapting the model for computer-aided diagnosis of polyps and lung nodules. [46][47] The Haralick texture model generates a total of 28 texture features from a region of interest (ROI) in a reconstructed image, for example, a nodule within the lung volume. 31 By treating the 28 Haralick texture features as a vector, the normalized Euclidean distance of the Haralick features between the reconstructed image and that of reference is selected as the texture metric, which was used to access the texture quality.…”

Section: F Texture-dose Characterization Strategymentioning

confidence: 99%

Characterization of tissue‐specific pre‐log Bayesian CT reconstruction by texture–dose relationship

et al. 2020

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Tissue textures have been recognized as biomarkers for various clinical tasks. In computed tomography (CT) image reconstruction, it is important but challenging to preserve the texture when lowering x-ray exposure from full-toward low-/ultra-low dose level. Therefore, this paper aims to explore the texture-dose relationship within one tissue-specific pre-log Bayesian CT reconstruction algorithm. Methods: To enhance the texture in ultra-low dose CT (ULdCT) reconstruction, this paper presents a Bayesian type algorithm. A shifted Poisson model is adapted to describe the statistical properties of pre-log data, and a tissue-specific Markov random field prior (MRFt) is used to incorporate tissue texture from previous full-dose CT, thus called SP-MRFt algorithm. Utilizing the SP-MRFt algorithm, we investigated tissue texture degradation as a function of x-ray dose levels from full dose (100 mAs/120 kVp) to ultralow dose (1 mAs/120 kVp) by using quantitative texture-based evaluation metrics. Results: Experimental results show the SP-MRFt algorithm outperforms conventional filtered back projection (FBP) and post-log domain penalized weighted least square MRFt (PWLS-MRFt) in terms of noise suppression and texture preservation. Comparable results are also obtained with shifted Poisson model with 7 × 7 Huber MRF weights (SP-Huber7). The investigation on texture-dose relationship shows that the quantified texture measures drop monotonically as dose level decreases, and interestingly a turning point is observed on the texture-dose response curve. Conclusions: This important observation implies that there exists a minimum dose level, at which a given CT scanner (hardware configuration and image reconstruction software) can achieve without compromising clinical tasks. Moreover, the experiment results show that the variance of electronic noise has higher impact than the mean to the texture-dose relationship.

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Section: F Texture-dose Characterization Strategymentioning

confidence: 99%

Characterization of tissue‐specific pre‐log Bayesian CT reconstruction by texture–dose relationship

et al. 2020

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“…Accuracy is the correct prediction from the whole dataset, sensitivity is the ability of the test to correctly identify patients with the disease, and the specificity of a clinical test refers to the ability of the test to correctly identify patients without the disease. The following commonly used validation measurements are shown [3].…”

Section: D Cnn Modelmentioning

confidence: 99%

“…Lung cancer is one of the critical diseases with rapidly rising morbidity and mortality rates, being the primary reason for cancer-related mortality around the world, with 1.8 million deaths annually [1]. Although the early detection rate has increased significantly, distinguishing malignant from benign tumors is one of the most challenging tasks [2,3]. Therefore, early and accurate diagnosis of lung cancer is essential.…”

Section: Introductionmentioning

confidence: 99%

Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

2020

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Lung cancer is one of the common causes of cancer deaths. Early detection and treatment of lung cancer is essential. However, the detection of lung cancer in patients produces many false positives. Therefore, increasing the accuracy of the classification of diagnosis or true detection by computed tomography (CT) is a difficult task. Solving this problem using intelligent and automated methods has become a hot research topic in recent years. Hence, we propose a 2D convolutional neural network (2D CNN) with Taguchi parametric optimization for automatically recognizing lung cancer from CT images. In the Taguchi method, 36 experiments and 8 control factors of mixed levels were selected to determine the optimum parameters of the 2D CNN architecture and improve the classification accuracy of lung cancer. The experimental results show that the average classification accuracy of the 2D CNN with Taguchi parameter optimization and the original 2D CNN in lung cancer recognition are 91.97% and 98.83% on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, and 94.68% and 99.97% on the International Society for Optics and Photonics with the support of the American Association of Physicists in Medicine (SPIE-AAPM) dataset, respectively. The proposed method is 6.86% and 5.29% more accurate than the original 2D CNN on the two datasets, respectively, proving the superiority of proposed model.

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“…For CT imaging [30], applications of CNN by transfer learning for electronic cleansing may improve accuracy from 89% to 94% for visualization of colorectal polyp images. Furthermore, the CNN developed by [31] showed improved colorectal polyp classification performance by area under the curve (AUC) of 86 and accuracy of 83% on CT image datasets. Deep learning has been popularly used since 1998 [14], when an early deep learning method named LeNet was created with a convolutional neural network (CNN) for recognizing digitized handwriting.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening

Jinsakul

Tsai

et al. 2019

Mathematics

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One of the leading forms of cancer is colorectal cancer (CRC), which is responsible for increasing mortality in young people. The aim of this paper is to provide an experimental modification of deep learning of Xception with Swish and assess the possibility of developing a preliminary colorectal polyp screening system by training the proposed model with a colorectal topogram dataset in two and three classes. The results indicate that the proposed model can enhance the original convolutional neural network model with evaluation classification performance by achieving accuracy of up to 98.99% for classifying into two classes and 91.48% for three classes. For testing of the model with another external image, the proposed method can also improve the prediction compared to the traditional method, with 99.63% accuracy for true prediction of two classes and 80.95% accuracy for true prediction of three classes.

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An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets

Cited by 38 publications

References 40 publications

Characterization of tissue‐specific pre‐log Bayesian CT reconstruction by texture–dose relationship

Characterization of tissue‐specific pre‐log Bayesian CT reconstruction by texture–dose relationship

Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

Enhancement of Deep Learning in Image Classification Performance Using Xception with the Swish Activation Function for Colorectal Polyp Preliminary Screening

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