Article
Oncology
Jeong Woo Son, Ji Young Hong, Yoon Kim, Woo Jin Kim, Dae-Yong Shin, Hyun-Soo Choi, So Hyeon Bak, Kyoung Min Moon
Summary: The early detection of lung nodules is important for patient treatment and follow-up. This paper provides guidelines for collecting lung nodule data and demonstrates the value of using collected data compared to a large-scale open dataset. The study also shows the effectiveness of transfer learning from pre-trained models and offers insights on the amount of data needed for stable lung nodule detection.
Article
Oncology
Zhenghua Zhang, Fang Yin, Shaolei Kang, Xiaoyu Tuo, Xiaodi Zhang, Dan Han
Summary: This study aims to analyze the value of SDCT electron density imaging in the detection of mixed ground-glass lung nodules (GGNs). It is found that SDCT can improve GGN visualization and increase the detection rate of mGGN compared with conventional CT.
JOURNAL OF CANCER RESEARCH AND CLINICAL ONCOLOGY
(2023)
Review
Automation & Control Systems
Shubham Dodia, B. Annappa, Padukudru A. Mahesh
Summary: Cancer is a leading cause of mortality and morbidity worldwide, and this paper discusses an overview of lung cancer and publicly available benchmark data sets for research purposes. It also compares recent research on medical image analysis of lung cancer using deep learning algorithms.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Biochemistry & Molecular Biology
Heng-Sheng Chao, Chiao-Yun Tsai, Chung-Wei Chou, Tsu-Hui Shiao, Hsu-Chih Huang, Kun-Chieh Chen, Hao-Hung Tsai, Chin-Yu Lin, Yuh-Min Chen
Summary: Low-dose computed tomography (LDCT) is an effective method for detecting early-stage lung cancer, but it has limitations in interpretation and accuracy. This study demonstrates that AI algorithm-assisted CT screening significantly improves the detection of lung nodules and ground glass nodules, leading to increased sensitivity and accuracy. The AI algorithm has a high standalone screening sensitivity and can be embedded into the hospital system for efficient clinical practice.
Article
Radiology, Nuclear Medicine & Medical Imaging
Jose Lucas Leite Calheiros, Lucas Benevides Viana de Amorim, Lucas Lins de Lima, Ailton Felix de Lima Filho, Jose Raniery Ferreira Junior, Marcelo Costa de Oliveira
Summary: Lung cancer is the most lethal malignant neoplasm globally, and while computer-aided diagnosis tools provide support, attributes from the perinodular zone are crucial in the classification of pulmonary nodules.
JOURNAL OF DIGITAL IMAGING
(2021)
Article
Computer Science, Interdisciplinary Applications
Zhan Gao, Qiuhao Zong, Yiqi Wang, Yan Yan, Yuqing Wang, Ning Zhu, Jin Zhang, Yunfu Wang, Liang Zhao
Summary: Liver vessel segmentation from computed tomography is a challenging task due to small vessel size and imbalanced distribution of vessels and liver tissues. This study proposes a sophisticated model and elaborated dataset, utilizing a newly conceived Laplacian salience filter and pyramid deep learning architecture. Experimental results show significant improvement over existing methods, achieving higher Dice scores on available datasets.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Francis T. Delaney, John G. Murray, Barry D. Hutchinson, Jim J. Egan, Michelle Murray, Sara Winward, Nicola Ronan, Carmel G. Cronin
Summary: The importance of lung cancer as a complication of lung transplantation is increasingly recognized. Radiology plays a key role in the assessment of lung cancer before and after transplantation. Chest CT can detect suspicious lung lesions and chronic lung structural diseases such as pulmonary fibrosis associated with an increased risk of malignancy after transplantation. However, the effectiveness of regular chest CT for lung cancer screening after transplantation needs further research.
EUROPEAN RADIOLOGY
(2022)
Article
Public, Environmental & Occupational Health
Jinglun Liang, Guoliang Ye, Jianwen Guo, Qifan Huang, Shaohui Zhang
Summary: Malignant pulmonary nodules are a main feature of lung cancer in early CT screening. With the application of deep learning in image processing, researchers are exploring methods to diagnose pulmonary nodules. Imbalanced datasets may lead to higher false-positives, but a filtering step in this study has shown promising results in reducing false-positives and achieving high accuracy.
FRONTIERS IN PUBLIC HEALTH
(2021)
Article
Computer Science, Interdisciplinary Applications
Quan Guo, Chengdi Wang, Jixiang Guo, Hongli Bai, Xiuyuan Xu, Lan Yang, Jianyong Wang, Nan Chen, Zihuai Wang, Yuncui Gan, Lunxu Liu, Weimin Li, Zhang Yi
Summary: A study was conducted on 1,000 LDCT scans for pulmonary nodule screening, comparing the performance of thick and thin scans. The study found that trained neural networks outperformed human doctors in detecting small nodules (<6.0mm), while human doctors had a slight advantage for larger nodules (>6.0mm). The combination of artificial intelligence and human doctors showed promise for achieving fast and accurate diagnosis.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Computer Science, Artificial Intelligence
Onika Akter, Mohammad Ali Moni, Mohammad Mahfuzul Islam, Julian M. W. Quinn, A. H. M. Kamal
Summary: Lung cancer is a major cause of cancer-related death, and early detection is crucial. This study improved image segmentation accuracy and utilized a neuro-fuzzy classifier to classify lung nodules, achieving good performance assessment results. The proposed methodology could potentially lead to better clinical outcomes for lung cancer patients.
APPLIED INTELLIGENCE
(2021)
Article
Biology
Shuang Liang, Huixiang Liu, Yu Gu, Xiuhua Guo, Hongjun Li, Li Li, Zhiyuan Wu, Mengyang Liu, Lixin Tao
Summary: The study proposes a deep learning framework to identify COVID-19 from medical images, achieving high diagnostic sensitivity through training convolutional neural networks. The method shows promise for computer-aided diagnosis of COVID-19 in clinical settings.
COMMUNICATIONS BIOLOGY
(2021)
Article
Engineering, Biomedical
Liyun Chen, Dongdong Gu, Yanbo Chen, Ying Shao, Xiaohuan Cao, Guocai Liu, Yaozong Gao, Qian Wang, Dinggang Shen
Summary: This study presents an artificial intelligence lung image analysis system (ALIAS) for nodule detection and segmentation, analyzing differences between benign and malignant lung nodules to enhance understanding of early lung cancer diagnosis.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2021)
Article
Engineering, Biomedical
Hassan Mkindu, Longwen Wu, Yaqin Zhao
Summary: This study proposes an automated computer-aided diagnosis scheme based on Vision Transformer architecture with Bayesian Optimisation for lung nodule detection to assist radiologists in decision-making. The empirical results prove the effectiveness of the proposed algorithm compared to the state-of-the-art methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Review
Oncology
Anton Schreuder, Ernst T. Scholten, Bram van Ginneken, Colin Jacobs
Summary: Lung cancer CT screening has shown a reduction in deaths but faces challenges such as false positives, cost-effectiveness, and radiologist availability. AI can enhance efficiency in screening, but more research is needed to fully integrate it into the analysis of lung CT scans.
TRANSLATIONAL LUNG CANCER RESEARCH
(2021)
Article
Engineering, Biomedical
Dongdong Gu, Guocai Liu, Zhong Xue
Summary: CT screening is effective in early detection of lung cancer, with image processing techniques and deep learning algorithms showing promising results in improving detection sensitivity and reducing false positives.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2021)
Article
Computer Science, Theory & Methods
Meiryani, Monika Sujanto, A. S. L. Lindawati, Arif Zulkarnain, Suryadiputra Liawatimena
Summary: This study analyzed auditors' perceptions of the implementation of technology transformation factors such as blockchain and CAATs, and their impact on audit quality at the Big Four Public Accounting Firm in Jakarta. The findings revealed that auditors' perception of blockchain implementation had a significant positive effect on audit quality, while their perception of CAATs implementation did not have a significant positive effect on audit quality.
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2021)
Proceedings Paper
Computer Science, Information Systems
Yuyanto, Suryadiputra Liawatimena
2018 6TH INTERNATIONAL CONFERENCE ON CYBER AND IT SERVICE MANAGEMENT (CITSM)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Suryadiputra Liawatimena, Yaya Heryadi, Lukas, Agung Trisetyarso, Antoni Wibowo, Bahtiar Saleh Abbas, Erland Barlian
2018 INDONESIAN ASSOCIATION FOR PATTERN RECOGNITION INTERNATIONAL CONFERENCE (INAPR)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Suryadiputra Liawatimena, Edi Abdurahman, Ford Lumban Gaol, Harco Leslie Hendric Spits Warnars, Benfano Soewito, Bahtiar Saleh Abbas, Agung Trisetyarso, Antoni Wibowo
2018 INDONESIAN ASSOCIATION FOR PATTERN RECOGNITION INTERNATIONAL CONFERENCE (INAPR)
(2018)
Proceedings Paper
Green & Sustainable Science & Technology
Wiedjaja Atmadja, Suryadiputra Liawatimena, Jonathan Lukas, Eka Putra Leo Nata, Ivan Alexander
INTERNATIONAL CONFERENCE ON ECO ENGINEERING DEVELOPMENT 2017 (ICEED 2017)
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Meiliana, Syaeful Karim, Suryadiputra Liawatimena, Agung Trisetyarso, Bahtiar Saleh Abbas, Wayan Suparta
2017 IEEE INTERNATIONAL CONFERENCE ON CYBERNETICS AND COMPUTATIONAL INTELLIGENCE (CYBERNETICSCOM)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Andrean Limanto, Azqa Fikri Khwarizma, Imelda, Reinert Yosua Rumagit, Victor Prasetya Pietono, Yohanes Halim, Suryadiputra Liawatimena
2017 5TH INTERNATIONAL CONFERENCE ON CYBER AND IT SERVICE MANAGEMENT (CITSM 2017)
(2017)
Article
Computer Science, Software Engineering
Suryadiputra Liawatimena, Jimmy Linggarjati
INTERNETWORKING INDONESIA
(2017)