International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
A REVIEW PAPER ON PULMONARY NODULE DETECTION Neethu AP1, Reena M Roy2 1PG
Student, Dept. of Electronics and Communication Engineering, LBTIW, Kerala, India Professor, Dept. of Electronics and Communication Engineering, LBTIW, Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------2Assistant
Abstract - Lung cancer is one of the major causes of
tomography (PET) scan, Sputum cytology etc. are used to detect the lung nodules early. The detection includes the grouping of tumours into two categories namely (i) nonCancerous (benign) and (ii) Cancerous (malignant).
cancer- related dead word wide and the early detection of lung cancer is the best way to increase the patient’s chance for survival. Accurate detection of malignant lung nodules from computed tomography (CT) scans is a difficult and time consuming task for radiologists. The size of the pulmonary nodules varies greatly and there are visual similarities with the structures such as blood vessels and shadows around the nodules, making it difficult and time consuming to accurately locate the nodules from the CT image. Hence it is a challenging task for radiologist to predict the abnormal nodules accurately. Differences in the shape and form of a lung nodule (up to 10 mm in diameter) may be lost through the manual detection process. Therefore, the computer aided diagnosis system helps radiologists to make a final decision immediately with greater accuracy and more confidence. Recently deep learning techniques used for lung nodule detection systems. The deep learning technique shows good performance and accurate result than traditional methods. This paper presents a review focusing on lung nodule detection in chest computed tomography (CT) images using different deep learning techniques and compares their results.
Unfortunately, most of the diagnoses occurs at the later stages of the disease and is largely due to a lack of earlystage symptoms and when diagnosed at the early stage of cancer, the probability of surviving increases. Differences in the shape and form of a lung nodule (up to 10 mm in diameter) may be lost through the manual detection process. The computer-assisted detection system with the help of constantly updated technology allows the radiologist to locate lung cancer tumors. It helps to improve the accuracy of detection of lung nodules, reduce the number of missing nodules and misdiagnosis. Recently deep learning neural network techniques used for lung nodule detection systems. Machine learning algorithms are limited in processing natural images in their raw form, and take a lot of time based on expert knowledge and tuning features. The deep learning techniques overcome these limitations. The deep learning neural network technique shows good performance and accurate result than traditional methods. It also shows promising performance in speech recognition, text recognition, computer-aided diagnosis, facial recognition, and drug detection.
Key Words: Computed tomography scan, Computedaided detection, pulmonary nodule detection.
1. INTRODUCTION
Nowadays deep learning algorithm is used in all areas, especially in medical image analysis, because of the multilevel abstraction and automatic extraction of features from large datasets. This paper presents a review focusing on lung nodule diagnosis in chest computed tomography (CT) images using different deep learning techniques and compares their results.
Lung Cancer is an uncontrolled growth of cell in the lung tissues, and they are malignant lung tumour and the early detection of lung cancer enhances the patient’s chance for survival. Lung Cancer is one of the deadly causes of disease with the death rate is 19.4%. The function of the lungs is to allow us to breathe. They bring oxygen into our body and expel carbon dioxide. Lung cancer occurs when the cells in the lungs are transformed or replaced, and various factors can cause this transformation. The main factors that cause this change in the lung cells are when people breathe in dangerous and toxic substances. The two main types of cancerous tumors are small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC). Smoking is one of the leading causes of lung cancer. It causes about 90 percentages of lung cancer cases. Tobacco smoke contains many chemicals that can cause lung cancer.
2. REVIEW ON DIFFERENT PAPERS In 2016, stetio et al. [1] introduced a Computer-aided detection system in CT scan based on multi-view convolutional networks. The network provides nodule candidates obtained by combining three candidate detectors designed for solid, sub solid and large nodules. The proposed system shows sensitivity from 85.7% to 93.3%.
There are several imaging methods such as Computed tomography (CT) scan, Chest X-Ray, Magnetic Resonance imaging (MRI), Sputum Cytology, Positron emission
© 2022, IRJET
|
Impact Factor value: 7.529
Hongyang Jiang et al. [2] suggests an effective lung nodule detection system based on multi-group patches cut from
|
ISO 9001:2008 Certified Journal
|
Page 246