scholarly journals Performance of a Deep Learning Algorithm Compared with Radiologic Interpretation for Lung Cancer Detection on Chest Radiographs in a Health Screening Population

Radiology ◽  
2020 ◽  
Vol 297 (3) ◽  
pp. 687-696 ◽  
Author(s):  
Jong Hyuk Lee ◽  
Hye Young Sun ◽  
Sunggyun Park ◽  
Hyungjin Kim ◽  
Eui Jin Hwang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Learning Algorithm ◽  
Health Screening ◽  
Chest Radiographs ◽  
Deep Learning Algorithm ◽  
Screening Population ◽  
Lung Cancer Detection

scholarly journals Enhanced Lung Cancer Detection using Deep Learning Algorithm

2020 ◽  
Vol 9 (7) ◽  
pp. 1016-1020
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Environmental Conditions ◽  
Learning Algorithm ◽  
The Other ◽  
Deep Learning Algorithm ◽  
The People ◽  
Sensitivity Specificity ◽  
Lung Cancer Detection

Lung cancer is more dangerous than any other cancer. Nowadays many people are affecting lung cancer because of their lifestyle and environmental conditions. The basic cause of lung cancer is smoking. Many steps are taken to avoid smoking but on the other way the cancer is affecting the people. In this paper, the Enhanced Deep Learning (EDL) based algorithm is introduced to detects cancer in lungs in various patients based on their symptoms. It is very important to detect the cancer in the earliers stages. The proposed system calculates the three parameters such as sensitivity, specificity and accuracy. Results show the performance of the proposed system.

scholarly journals Deep Learning Based Lung Cancer Detection and Classification

2020 ◽  
Vol 994 ◽  
pp. 012026
Author(s):  
N Kalaivani ◽  
N Manimaran ◽  
Dr. S Sophia ◽  
D D Devi
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Lung Cancer Detection

scholarly journals Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

Cancers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 4585
Author(s):  
Wouter R. P. H. van de Worp ◽  
Brent van der Heyden ◽  
Georgios Lappas ◽  
Ardy van Helvoort ◽  
Jan Theys ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Tumor Growth ◽  
Tumor Volume ◽  
Learning Algorithm ◽  
Lung Tumor ◽  
Whole Body ◽  
Ct Scans ◽  
Therapeutic Effects ◽  
Deep Learning Algorithm

Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

Deep learning for lung Cancer detection and classification

2020 ◽  
Vol 79 (11-12) ◽  
pp. 7731-7762 ◽  
Author(s):  
A. Asuntha ◽  
Andy Srinivasan
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Lung Cancer Detection

scholarly journals A Medical Service Application Based on 3D-CNN and Knowledge Graph

2021 ◽  
Vol 2078 (1) ◽  
pp. 012048
Author(s):  
Jiasheng Ni
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Semantic Segmentation ◽  
Ct Images ◽  
Knowledge Graph ◽  
Medical Response ◽  
Learning Techniques ◽  
3D Cnn ◽  
Lung Cancer Detection

Abstract Remote medical prognosis application is a category of medical tests tool designed to collect patients’ body conditions and offer diagnosis results synchronously. However, most online applications are predicated on a simple chat bot which typically redirect patients to other online medical websites, which undermines the user experience and may prompt useless information for their reference. To tackle these issues, this paper proposed a medical prognosis application with deep learning techniques for a more responsive and intelligent medical prognosis procedure. This application can be break down into three parts-lung cancer detection, a database-supporting medical QA bot and a Hierarchical Bidirectional LSTM model (HBDA). A 3D-CNN model is built for the lung cancer detection, with a sequence of sliced CT images as inputs and outputs a probability scaler for tumor indications. A knowledge graph is applied in the medical QA bot implementation and the HBDA model is designed for semantic segmentation in order to better capture users’ intention in medical questions. For the performance of the medical prognosis, since we have limited computer memory, the 3D-CNN didn’t perform very well on detecting tumor conditions in the CT images with accuracy at around 70%. The knowledge graph-based medical QA bot intelligently recognize the underlying pattern in patients’ question and delivered decent medical response. The HBDA model performs well on distinguish the similarities and disparities between various medical questions, reaching accuracy at 90%. These results shed light for the feasibility of utilizing deep learning techniques such as 3D-CNN, Knowledge Graph, and Hierarchical Bi-directional LSTM to simulate the medical prognosis process.

scholarly journals A Survey on Multistage lung cancer Detection and Classification

2020 ◽  
pp. 313-320
Author(s):  
Jay Jawarkar ◽  
Nishit Solanki ◽  
Meet Vaishnav ◽  
Harsh Vichare ◽  
Sheshang Degadwala
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Small Cell Lung Cancer ◽  
Cancer Detection ◽  
Cell Lung Cancer ◽  
Small Cell ◽  
The Body ◽  
Future Application ◽  
Small Cell Lung ◽  
Lung Cancer Detection

Earlier, Lung cancer is the primary cause of cancer deaths worldwide among both men and women, with more than 1 million deaths annually. Lung Cancer have been widest difficulty faced by humans over recent couple of decades. When a person has lung cancer, they have abnormal cells that cluster together to form a tumor. A cancerous tumor is a group of cancer cells that can grow into and destroy nearby tissue. It can also spread to other parts of the body. There are two main types of lung cancer:1. Non-small cell lung cancer, 2. Small cell lung cancer. Non- small cell lung cancer has four main stages. In this research we are classifying four stages of lung cancer. Lung cancer detection at early stage has become very important. Currently many techniques are used based on image processing and deep learning techniques for lung cancer classification. For that lung patient Computer Tomography (CT) scan images are used to detect and lung nodules and classify lung cancer stage of that nodules. In this re- search we compare different Machine learning (SVM, KNN, RF etc.) techniques with deep learning (CNN, CDNN) techniques using different parameters accuracy, precision and recall. In this Research paper we com- pare all existing approach and find our better result for future application.

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