Title:Segmentation and Classification of Lung Cancer: A Review
Volume: 16
Issue: 2
Author(s): Javeria Amin, Muhammad Sharif and Mussarat Yasmin
Affiliation:
Keywords:
Computer-aided diagnosis, computed tomography images, lung cancer, modalities, positron
emission tomography, segmentation.
Abstract: Lung cancer is one of the most common diseases globally, causing almost 1.3
million deaths in a year. 80% people get affected via Non-small cell lung cancer and about
20% to 30% are patients who have brain metastases. The prognosis of such patients is very
small, even less than six months. The epidermal growth factor receptor is expressed over
60% with Non-small cell lung cancer patients. In males this cancer type is most common,
but significantly increasing in women as well. 85% to 90% of this type is malignant for
lung neoplasm. Lung cancer study in 2012 revealed that the first death in Poland due to
cancer and the United Kingdom was of a woman. Many treatments and diagnoses are
nowadays available to deal with lung cancer. This paper encapsulates a description of lung
cancer, its types and immense contribution of imaging techniques for its detection. It
mainly describes different types of segmentation and classification techniques used by
computer aided diagnosis systems for lung cancer detection. Further, the paper elaborates a
discussion on experiments performed by authors for cancer detection. This work will be
useful for the specialists and researchers working in the related field.
