Lung Cancer Detection on CT Scan Medical Image

Abstract

Currently, lung cancer is one of the deadly diseases in the world. Various investigations are underway to
reduce this disease. The lung disease imaging system produces large amounts of medical images (such as CT scan
image, X-ray, MRI images etc.) that containing vast amounts of information. It is very difficult for doctors and
clinicians to interpreting and identifying this information accurately. Medical image analysis has become a major
research area in the healthcare centre. The purpose of lung cancer detection system is able to detect and provide
reliable information to doctors and clinicians from a medical image. To minimize this problem, many systems have
been proposed by using different image processing techniques, machine learning, and deep learning techniques.
Machine learning is one of the most popular techniques that used in computer-aided diagnosis CAD) and medical
image analysis in the classification of the objects such as lesions to certain lesions (example lesions or non-lesion, and
malignant or benign) based on input features obtained from segmented objects. The recent advent of deep learning
has replaced many other machine learning methods, because it avoids the creation of hand-engineering features, thus
removing a critical source of error from the process. Deep learning is a breakthrough in machine learning techniques
that have overcome the field of pattern recognition and computer vision research areas. Deep learning provides the
machine learning that high-level abstraction features from a medical image but not use handcrafted features. Deep
learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future
applications. In the medical image analysis, there are various application areas including detection, segmentation,
classification, and computer-aided diagnosis. The main aim of the paper is reviewing the recent literature and finding
the gaps of the proposed system that related to lung cancer detection in medical image and providing the future work.