Article
Computer Science, Interdisciplinary Applications
Yu Han, Honggang Qi, Ling Wang, Chen Chen, Jun Miao, Hongbo Xu, Ziqi Wang, Zhijun Guo, Qian Xu, Qiang Lin, Haitao Liu, Junying Lu, Fei Liang, Wenqiu Feng, Haiyan Li, Yan Liu
Summary: In this study, a Pulmonary Nodules Detection Assistant Platform was designed for early detection and classification of lung nodules based on physical examination LDCT images. The system utilizes various computer aided diagnosis methods to detect and classify nodules of different sizes, achieving automatic and efficient detection.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Prabaharan Sengodan, Karthik Srinivasan, Rajaram Pichamuthu, Saravanan Matheswaran
Summary: This paper proposes a novel approach called MNPS-MEFL to improve the accuracy of lung cancer prediction by preprocessing and optimizing the parameters. The proposed method is evaluated using the LIDC-IDRI dataset and achieves a classification accuracy of 98.53%. This approach has the potential to improve the accuracy of lung cancer prognosis and benefit lung disorder patients.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Oncology
Jing-Hang Ma, Shang-Feng You, Ji-Sen Xue, Xiao-Lin Li, Yi-Yao Chen, Yan Hu, Zhen Feng
Summary: Computer-aided diagnosis system plays an important role in cervical lesion diagnosis by using auto-segmented colposcopic images to extract features, augmenting minority data, and generating preliminary diagnosis results. The system improves sensitivity while maintaining acceptable specificity and accuracy.
FRONTIERS IN ONCOLOGY
(2022)
Article
Public, Environmental & Occupational Health
Hongfeng Wang, Hai Zhu, Lihua Ding
Summary: This study proposes a deep convolutional neural network technique called TransUnet for the automatic classification of lung nodules. The experimental results demonstrate that the method performs well in classifying lung nodules and has potential application in diagnosing lung cancer.
FRONTIERS IN PUBLIC HEALTH
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
T. -W. Tang, W. -Y. Lin, J. -D. Liang, K. -M. Li
Summary: This study aims to develop a high-accuracy low-dose computed tomography (LDCT) lung nodule diagnosis system by combining artificial intelligence (AI) technology with the Lung CT Screening Reporting and Data System (Lung-RADS) for future AI-aided diagnosis of pulmonary nodules. The study compares and selects the best deep-learning segmentation method and uses the Image Biomarker Standardization Initiative (IBSI) for feature extraction and reduction. The performance of the established system shows promising results.
CLINICAL RADIOLOGY
(2023)
Article
Engineering, Electrical & Electronic
Rasool Baghbani, Mohammad Behgam Shadmehr, Masoomeh Ashoorirad, Seyyedeh Fatemeh Molaeezadeh, Mohammad Hassan Moradi
Summary: This study introduces a simple and safe method to localize in-depth pulmonary nodules intraoperatively, using a bioimpedance probe with spherical electrodes. By analyzing bioimpedance data, a smart system was designed to differentiate between healthy and tumoral lung tissue with high accuracy. This research shows the feasibility of designing a real-time, safe, and smart system to localize invisible/impalpable pulmonary nodules using the bioimpedance spectrum of lung tissue.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Biomedical
Rekka Mastouri, Nawres Khlifa, Henda Neji, Saoussen Hantous-Zannad
Summary: The study introduced a new classification approach using bilinear convolutional neural network (BCNN) combined with support vector machine (SVM) to reduce false positives in the classification of lung nodules on CT images, achieving high accuracy and AUC rates on a public database.
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
(2021)
Article
Oncology
Yuchong Zhang, Hui Qu, Yumeng Tian, Fangjian Na, Jinshan Yan, Ying Wu, Xiaoyu Cui, Zhi Li, Mingfang Zhao
Summary: This study investigates the correlation between CT imaging features and pathological subtypes of pulmonary nodules, and constructs a prediction model using deep learning. The model demonstrates satisfactory accuracy in predicting pathological subtypes of pulmonary nodules and shows potential for use in clinical practice.
Article
Biology
Yu Gu, Jingqian Chi, Jiaqi Liu, Lidong Yang, Baohua Zhang, Dahua Yu, Ying Zhao, Xiaoqi Lu
Summary: This paper summarizes the CAD approaches using deep learning for lung nodule detection on CT scan data. The technologies show promising results in improving the survival rate of lung cancer patients, but there are still challenges and limitations to be addressed.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Information Systems
Murat Dener, Sercan Gulburun
Summary: A model combining supervised and unsupervised learning algorithms is proposed to predict malware using clustering to enhance the performance of supervised classifiers. The model achieves high accuracy and f1 scores on the BODMAS, EMBER 2018, and Kaggle datasets. The tiered positioning of classifiers significantly reduces prediction time.
CMC-COMPUTERS MATERIALS & CONTINUA
(2023)
Article
Mathematical & Computational Biology
Yifei Xu, Shijie Wang, Xiaoqian Sun, Yanjun Yang, Jiaxing Fan, Wenwen Jin, Yingyue Li, Fangchu Su, Weihua Zhang, Qingli Cui, Yanhui Hu, Sheng Wang, Jianhua Zhang, Chuanliang Chen
Summary: This study proposed a method based on ensemble learning to distinguish between malignant and benign pulmonary nodules, showing that the ensemble learning model is more effective than YOLOv3 network and CNN in reducing false positives and has a higher accuracy in identifying pulmonary nodules.
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
(2022)
Article
Engineering, Biomedical
K. V. Deepak, R. Bharanidharan
Summary: This study proposes a new ensemble learning model for bone tumor classification using machine learning algorithms. Through preprocessing and feature extraction, and utilizing models like KSVM, IANN_BSO, and OKELM for classification, the study achieves accurate classification and severity analysis of bone tumors.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Oncology
Sheng-Chieh Hung, Yao-Tung Wang, Ming-Hseng Tseng
Summary: This study presents an AI system for the automatic diagnosis of lung cancer based on lung nodule images from CT scans. The system utilizes a 3D interpretable hierarchical semantic convolutional neural network (HSNet) to recognize different features of lung nodules, achieving better performance than previous methods.
Article
Biology
Anca Emanuela Musetescu, Florin Liviu Gherghina, Lucian-Mihai Florescu, Liliana Streba, Paulina Lucia Ciurea, Alesandra Florescu, Ioana Andreea Gheonea
Summary: The study used CAD technology to detect lung nodules in patients with rheumatoid arthritis, and found that CAD can play a role in reducing the interpretation time of CT examinations, but there are also certain false positive and false negative rates.
Article
Computer Science, Artificial Intelligence
Tuanfei Zhu, Cheng Luo, Zhihong Zhang, Jing Li, Siqi Ren, Yifu Zeng
Summary: This paper introduces a structure-preserving Oversampling method for high-dimensional imbalanced time series classification, OHIT, and integrates it into boosting framework to form a new ensemble algorithm OHITBoost. Extensive experiments on several publicly available time-series datasets demonstrate their effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
