Lung nodules detection using semantic segmentation and classification with optimal features
Autor: | Hafiz Tayyab Rauf, Talha Meraj, Syed Ahmad Chan Bukhari, Arslan Hassan, M. Ikramullah Lali, Saliha Zahoor, Liaqat Ali, Umar Shoaib |
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Rok vydání: | 2020 |
Předmět: |
0209 industrial biotechnology
Lung Computer science business.industry Early detection Cancer Pattern recognition 02 engineering and technology medicine.disease Thresholding 020901 industrial engineering & automation medicine.anatomical_structure Artificial Intelligence Principal component analysis 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Segmentation Artificial intelligence business Lung cancer Classifier (UML) Software |
Zdroj: | Neural Computing and Applications. 33:10737-10750 |
ISSN: | 1433-3058 0941-0643 |
Popis: | Lung cancer is a deadly disease if not diagnosed in its early stages. However, early detection of lung cancer is a challenging task due to the shape and size of its nodules. Radiologists use automated tools for more precise opinion. Automated detection of the affected lung nodules is complicated because of the shape similarity among healthy and unhealthy tissues. Over the years, several expert systems have been developed that help radiologists to diagnose lung cancer effectively. In this article, we have proposed a framework to precisely detect lungs cancer to classify the benign and malignant nodules. The proposed framework is tested using the subset of the publicly available dataset, i.e., the Lung Image Database Consortium image collection (LIDC-IDRI). We applied filtering and noise removal in the pre-processing phase. Furthermore, the adaptive thresholding technique (OTSU) and the semantic segmentation are used to accurately detect the unhealthy lung nodules. Overall, 13 nodules features have extracted using principal components analysis algorithm. In addition, four optimal features are selected based on the classification performance. In the classification phase, 9 different classifiers are employed for the experimentation. Empirical analysis shows that the proposed system outperformed other techniques and provides 99.23% accuracy using a logit boost classifier. |
Databáze: | OpenAIRE |
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