Bruntha, Malin, Alex Pandian, Immanuel, Abraham, Siril Sam. (1399). Classification of Lung Nodule Using Hybridized Deep Feature Technique. , 12(Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications.), 109-128. doi: 10.22059/jitm.2020.79369
Malin Bruntha; Immanuel Alex Pandian; Siril Sam Abraham. "Classification of Lung Nodule Using Hybridized Deep Feature Technique". , 12, Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications., 1399, 109-128. doi: 10.22059/jitm.2020.79369
Bruntha, Malin, Alex Pandian, Immanuel, Abraham, Siril Sam. (1399). 'Classification of Lung Nodule Using Hybridized Deep Feature Technique', , 12(Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications.), pp. 109-128. doi: 10.22059/jitm.2020.79369
Bruntha, Malin, Alex Pandian, Immanuel, Abraham, Siril Sam. Classification of Lung Nodule Using Hybridized Deep Feature Technique. , 1399; 12(Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications.): 109-128. doi: 10.22059/jitm.2020.79369

Classification of Lung Nodule Using Hybridized Deep Feature Technique

Journal of Information Technology Management
دوره 12، Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications.، 2020، صفحه 109-128    اصل مقاله (848.26 K)
نوع مقاله: Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications
شناسه دیجیتال (DOI): 10.22059/jitm.2020.79369
نویسندگان
Malin Bruntha* 1؛ Immanuel Alex Pandian1؛ Siril Sam Abraham2
1Assistant Prof., Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore – 641114, Tamil Nadu, India.
2Computer Vision Intern, Vasundharaa Geo Technologies, Pune, Maharashtra, India.
چکیده
Deep learning techniques have become very popular among Artificial Intelligence (AI) techniques in many areas of life. Among many types of deep learning techniques, Convolutional Neural Networks (CNN) can be useful in image classification applications. In this work, a hybridized approach has been followed to classify lung nodule as benign or malignant. This will help in early detection of lung cancer and help in the life expectancy of lung cancer patients thereby reducing the mortality rate by this deadly disease scourging the world. The hybridization has been carried out between handcrafted features and deep features. The machine learning algorithms such as SVM and Logistic Regression have been used to classify the nodules based on the features. The dimensionality reduction technique, Principle Component Analysis (PCA) has been introduced to improve the performance of hybridized features with SVM. The experiments have been carried out with 14 different methods. It has been found that GLCM + VGG19 + PCA + SVM outperformed all other models with an accuracy of 94.93%, sensitivity of 90.9%, specificity of 97.36% and precision of 95.44%. The F1 score was found to be 0.93 and the AUC was 0.9843. The False Positive Rate was found to be 2.637% and False Negative Rate was 9.09%.
کلیدواژه‌ها
CNN؛ Transfer Learning؛ GLCM؛ SVM؛ PCA
مراجع
آمار
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