Deep Learning Methodology of Lung Cancer Detection and Diagnosis Using CT Images: A Systematic Approach
Keywords:
Lung Cancer Detection, Image Pre-Processing, Image Segmentation, Convo-lution Neural NetworkAbstract
In the present era lung nodule is the very dangerous and deadly cancer disease requires initial diagnosis which improves patient survival probability. Many techniques have played the major and important role in the medical field in detection of lung cancer by analyzing lung medical image. Here we are carried out a systematic article surveys of different article published from last five years. The four different databases like (Science Direct, Scopus, web of science, and IEEE), used during last five years and chosen around 15 articles to carry out systematic survey on lung cancer detection. The major goal of the survey work is to consolidate and concise recent advancement in lung cancer detection medical field, diagnosis considering various detection algorithms and methods. This article concise and summarizes the deep knowledge by addressing the results and findings of recent research which enhances and provides sufficient knowledge in the relevant field. We included challenges, applications, and recommendation for further enhancements after analyzing different research articles in detail. The lung image screening, diagnosis of cancer using CT imaging, in which detailed scanned images of lung is included. Further enhancements by using of CAS (“computer-assisted systems”) DL concepts were promoted to interpret lung nodule detection of CT images. The goal of this article is to cover the over-view related to DL techniques, detection of lung cancer using DL techniques and its related applications and benefits. This paper mainly focuses on two different methods of DL while screening and diagnosis of lung cancer, like classification and segmentation methods. The shortcomings and benefits, advancement of DL models are also be elaborated, the analysis picturises and demonstrates there is a potential significance of use of DL techniques to enable and provide accurate, precise, effective- computer assist for lung cancer diagnosis and screening by considering CT images. The part of this review article a set of potential recommendations for future scope and work which improves available applications of DL methods to enhances the better and efficient accurate results in identification of lung cancer.
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