Classification of Lung Nodule Using Hybridized Deep Feature Technique
سال انتشار: 1399
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 73
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شناسه ملی سند علمی:
JR_JITM-12-5_008
تاریخ نمایه سازی: 25 بهمن 1400
چکیده مقاله:
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 ۱۴ different methods. It has been found that GLCM + VGG۱۹ + PCA + SVM outperformed all other models with an accuracy of ۹۴.۹۳%, sensitivity of ۹۰.۹%, specificity of ۹۷.۳۶% and precision of ۹۵.۴۴%. The F۱ score was found to be ۰.۹۳ and the AUC was ۰.۹۸۴۳. The False Positive Rate was found to be ۲.۶۳۷% and False Negative Rate was ۹.۰۹%.
کلیدواژه ها:
نویسندگان
Bruntha
Assistant Prof., Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore – ۶۴۱۱۱۴, Tamil Nadu, India.
Alex Pandian
Assistant Prof., Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore – ۶۴۱۱۱۴, Tamil Nadu, India.
Abraham
Computer Vision Intern, Vasundharaa Geo Technologies, Pune, Maharashtra, India.
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