Deep Learning for Categorization of Lung Cancer CT Images

Rossetto, Allison M.; Zhou, Wenjing

doi:10.1109/chase.2017.98

Cited by 31 publications

(15 citation statements)

References 1 publication

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“…In Table 2, we can see that the best accuracy of the regular CDNN is 70%, and the best accuracy of the 76%, thus we used 73% as the minimal threshold value for certainty for cancer detection. Taking 73% as threshold for cancerous for both topologies and using this value as a threshold for cancerous, we can see in Figure 8 that our double CDNN detected cancer in stage 3, whereas the regular DNN from [28] did not detect cancer even in stage 4 (late stage). Taking the lower threshold value of 70% (Table 2) We tested these images with standard Convolution Deep Neural Network, used by the authors of [28], against our double Convolution pre-clustered Deep Neural Network with edge sharpening filters.…”

Section: Comparison Of the Regular Against Double Cdnnmentioning

confidence: 96%

“…Taking 73% as threshold for cancerous for both topologies and using this value as a threshold for cancerous, we can see in Figure 8 that our double CDNN detected cancer in stage 3, whereas the regular DNN from [28] did not detect cancer even in stage 4 (late stage). Taking the lower threshold value of 70% (Table 2) We tested these images with standard Convolution Deep Neural Network, used by the authors of [28], against our double Convolution pre-clustered Deep Neural Network with edge sharpening filters. We used this test set of images to determine the threshold or Tx stage in which both networks can detect possibility of cancer.…”

Section: Comparison Of the Regular Against Double Cdnnmentioning

confidence: 96%

“…Image recognition in Deep Neural Networks is based on image classification, where the Neural Network is trained to classify an image into a list of predetermined piles or types, [28]. In its simplest form, it is used to determine if something is recognized or not.…”

Section: Medical Image Classification Using Double Dnnmentioning

confidence: 99%

“…We tested these images with standard Convolution Deep Neural Network, used by the authors of [28], against our double Convolution pre-clustered Deep Neural Network with edge sharpening filters. We used this test set of images to determine the threshold or Tx stage in which both networks can detect possibility of cancer.…”

Section: Comparison Of the Regular Against Double Cdnnmentioning

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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Section: Comparison Of the Regular Against Double Cdnnmentioning

confidence: 96%

Section: Comparison Of the Regular Against Double Cdnnmentioning

confidence: 96%

Section: Medical Image Classification Using Double Dnnmentioning

confidence: 99%

Section: Comparison Of the Regular Against Double Cdnnmentioning

confidence: 99%

See 2 more Smart Citations

Using Double Convolution Neural Network for Lung Cancer Stage Detection

Jakimovski

Davčev

2019

Applied Sciences

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“…The application of artificial intelligence (AI) in cancer diagnosis is no longer new; numerous studies have already been applied to address the most prevalent cancers such as lung cancer [8][9][10], thyroid cancer [11], ovarian cancer [12,13] and breast cancer [14][15][16][17]. Most studies make use of image-based data such as MRI images, computed tomography (CT) scans, positron emission tomography (PET) scans, X-rays, and H&E-stained biopsy images, which is the gold standard [18].…”

Section: Introductionmentioning

confidence: 99%

Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks

et al. 2022

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In this study, three (3) neural networks (NN) were designed to discriminate between malignant (n = 78) and benign (n = 88) breast tumors using their respective attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral data. A proposed NN-based sensitivity analysis was performed to determine the most significant IR regions that distinguished benign from malignant samples. The result of the NN-based sensitivity analysis was compared to the obtained results from FTIR visual peak identification. In training each NN models, a 10-fold cross validation was performed and the performance metrics–area under the curve (AUC), accuracy, positive predictive value (PPV), specificity rate (SR), negative predictive value (NPV), and recall rate (RR)–were averaged for comparison. The NN models were compared to six (6) machine learning models–logistic regression (LR), Naïve Bayes (NB), decision trees (DT), random forest (RF), support vector machine (SVM) and linear discriminant analysis (LDA)–for benchmarking. The NN models were able to outperform the LR, NB, DT, RF, and LDA for all metrics; while only surpassing the SVM in accuracy, NPV and SR. The best performance metric among the NN models was 90.48% ± 10.30% for AUC, 96.06% ± 7.07% for ACC, 92.18 ± 11.88% for PPV, 94.19 ± 10.57% for NPV, 89.04% ± 16.75% for SR, and 94.34% ± 10.54% for RR. Results from the proposed sensitivity analysis were consistent with the visual peak identification. However, unlike the FTIR visual peak identification method, the NN-based method identified the IR region associated with C–OH C–OH group carbohydrates as significant. IR regions associated with amino acids and amide proteins were also determined as possible sources of variability. In conclusion, results show that ATR-FTIR via NN is a potential diagnostic tool. This study also suggests a possible more specific method in determining relevant regions within a sample’s spectrum using NN.

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Detection of Lung Cancer from CT Images Using Image Processing

Sheeba

Judin

2022

Intelligent Systems Design and Applications

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Deep Learning for Categorization of Lung Cancer CT Images

Cited by 31 publications

References 1 publication

Using Double Convolution Neural Network for Lung Cancer Stage Detection

Using Double Convolution Neural Network for Lung Cancer Stage Detection

Detection of breast cancer by ATR-FTIR spectroscopy using artificial neural networks

Detection of Lung Cancer from CT Images Using Image Processing

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