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

Lin, Cheng‐Jian; Jeng, Shiou-Yun; Chen, Mei-Kuei

doi:10.3390/app10072591

Cited by 47 publications

(26 citation statements)

References 23 publications

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“…To verify the higher efficiency of the proposed method, a comparison analysis of the method has been applied toward some state-of-the-art algorithms, including Kavitha's [38], Kumar's [39], and Lin's [40], applied to the Lung CT-Diagnosis database.…”

Section: Discussionmentioning

confidence: 99%

Lung Cancer Diagnosis Based on an ANN Optimized by Improved TEO Algorithm

Rong

Rezaei²

2021

Computational Intelligence and Neuroscience

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A quarter of all cancer deaths are due to lung cancer. Studies show that early diagnosis and treatment of this disease are the most effective way to increase patient life expectancy. In this paper, automatic and optimized computer-aided detection is proposed for lung cancer. The method first applies a preprocessing step for normalizing and denoising the input images. Afterward, Kapur entropy maximization is performed along with mathematical morphology to lung area segmentation. Afterward, 19 GLCM features are extracted from the segmented images for the final evaluations. The higher priority images are then selected for decreasing the system complexity. The feature selection is based on a new optimization design, called Improved Thermal Exchange Optimization (ITEO), which is designed to improve the accuracy and convergence abilities. The images are finally classified into healthy or cancerous cases based on an optimized artificial neural network by ITEO. Simulation is compared with some well-known approaches and the results showed the superiority of the suggested method. The results showed that the proposed method with 92.27% accuracy provides the highest value among the compared methods.

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Section: Discussionmentioning

confidence: 99%

Lung Cancer Diagnosis Based on an ANN Optimized by Improved TEO Algorithm

Rong

Rezaei²

2021

Computational Intelligence and Neuroscience

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“…(6) Among the deep learning methods, convolutional neural networks (CNNs) have achieved considerable success in image analysis applications, such as object detection, facial recognition, and medical image classification. (7)(8)(9) CNNs usually consist of fully connected layers (e.g., input layer and output layer), convolutional layers, and pooling layers for downsampling. (10) CNNs are the most commonly used deep learning architecture, and numerous CNN architectures, such as LeNet-5, AlexNet, and GoogleNet, are available.…”

Section: Introductionmentioning

confidence: 99%

“…The problem of the complicated parameter setting process in CNNs can be overcome by using the Taguchi method, which can obtain the optimum parameters in a relatively short time with a relatively low cost and a small number of experiments. (21) Lin et al (9) used the Taguchi method to determine the optimum parameter combination for a CNN structure to improve the accuracy of lung cancer classification. On the basis of the aforementioned discussion, in this paper, we propose a CNN with Taguchi parametric optimization for facial image detection applications.…”

Section: Introductionmentioning

confidence: 99%

Integrated Image Sensor and Hyperparameter Optimization of Convolutional Neural Network for Facial Skin Detection

Lin¹,

Lin²,

Jeng³

et al. 2021

Sensors and Materials

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Detection of the facial skin condition enables people to better understand skin problems and helps them select appropriate treatment methods and maintenance products. We collected facial images of different skin problems by using an image optical sensor. To overcome the problem of insufficient training data, the collected dataset was expanded through data augmentation. In the field of computer vision, deep learning is often used for solving image recognition problems with high accuracy. Therefore, we used a convolutional neural network (CNN) to detect facial images of different skin problems. To overcome the difficulty of parameter selection and increase the recognition rate and robustness of the CNN, the parameters of the CNN were optimized using the Taguchi method. Eight control factors in the convolutional layer and the L36 orthogonal array (OA) were used in experiments. Analysis of variance was used for statistical analysis in the design of the experiments to obtain the optimal parameter combination for the developed CNNs. The experimental results indicate that the CNN optimized using the Taguchi method had an accuracy of 86.95%. The accuracy of the optimized CNN was 7.24% higher than that of the original CNN. The experimental results prove that the proposed hyperparameter optimization method can effectively improve the accuracy of network detection.

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“…An example of efficient use of CNN to assist the medical diagnosis of lung diseases is the work by Lin et al [ 27 ]. They developed a 2D CNN study with parametric optimization Taguchi [ 28 ] to recognize lung cancer from CT scans automatically.…”

Section: Introductionmentioning

confidence: 99%

Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation

Souza

Silva²,

Marques³

et al. 2020

Sensors

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Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million deaths and over 10.4 million new occurrences. The detection of lung regions in images is a classic medical challenge. Studies show that computational methods contribute significantly to the medical diagnosis of lung pathologies by Computerized Tomography (CT), as well as through Internet of Things (IoT) methods based in the context on the health of things. The present work proposes a new model based on IoT for classification and segmentation of pulmonary CT images, applying the transfer learning technique in deep learning methods combined with Parzen’s probability density. The proposed model uses an Application Programming Interface (API) based on the Internet of Medical Things to classify lung images. The approach was very effective, with results above 98% accuracy for classification in pulmonary images. Then the model proceeds to the lung segmentation stage using the Mask R-CNN network to create a pulmonary map and use fine-tuning to find the pulmonary borders on the CT image. The experiment was a success, the proposed method performed better than other works in the literature, reaching high segmentation metrics values such as accuracy of 98.34%. Besides reaching 5.43 s in segmentation time and overcoming other transfer learning models, our methodology stands out among the others because it is fully automatic. The proposed approach has simplified the segmentation process using transfer learning. It has introduced a faster and more effective method for better-performing lung segmentation, making our model fully automatic and robust.

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

Cited by 47 publications

References 23 publications

Lung Cancer Diagnosis Based on an ANN Optimized by Improved TEO Algorithm

Lung Cancer Diagnosis Based on an ANN Optimized by Improved TEO Algorithm

Integrated Image Sensor and Hyperparameter Optimization of Convolutional Neural Network for Facial Skin Detection

Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation

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