A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans

Cao, Wenming; Wu, Rui; Cao, Guitao; He, Zhihai

doi:10.1109/access.2020.3018666

Cited by 34 publications

(26 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…U-net). The 2017 Kaggle Data Science Bowl, with $1 million in awards and also more than 1,000 competing groups, has as its goal predicting the likelihood that a person has lung disease from a Computed Tomography Scans [ 192 – 197 ]. COVID-19 Lung CT Lesion Segmentation Challenge - 2020 organized by MICCAI and more than 1976 teams are participating to predict the COVID-19.…”

Section: Anatomical Domains Of Medical Imagesmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

show abstract

Section: Anatomical Domains Of Medical Imagesmentioning

confidence: 99%

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal

Qureshi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…Several technical breakthroughs and applications of new technologies were achieved in the AlexNet network. It is these techniques that take convolutional neural networks to new heights [6].…”

Section: State Of the Artmentioning

confidence: 99%

Application of Recurrent Neural Network Algorithm in Intelligent Detection of Clinical Ultrasound Images of Human Lungs

Jia

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Lung ultrasound has great application value in the differential diagnosis of pulmonary exudative lesions. It has good sensitivity and specificity for the diagnosis of various pulmonary diseases in neonates and children. It is believed that it can replace chest CT examination. It is routinely used for the diagnosis of pulmonary diseases in emergency critical care medicine. However, the interpretation of the impact of ultrasound on the human lungs relies heavily on experienced physicians, which greatly restricts the interpretation efficiency of the impact of ultrasound. In order to improve the efficiency of monitoring and interpretation of the impact of ultrasound, this paper proposes an intelligent detection algorithm for human lung clinical ultrasound images based on recurrent neural network. Transfer learning is used to replace the fully connected layer of the VGG16 model and improve the loss function, so that the same the Euclidean distance between category images can be reduced, and the Euclidean distance between different categories of images can be increased, enhancing the resolution of the entire model, thereby achieving better image feature extraction results. The experimental results show that the algorithm proposed in this paper can surpass the doctor’s level in the identification of various lung diseases.

show abstract

“…However, in recent years, a number of researchers have created deep learning-based lung detection techniques. One of these ways is the convolutional neural network (CNN) approach, It has exceptional computer vision performance values, and increases the CADe system's accuracy and sensitivity [34]. Trends for the years 2019-2022 include deep learning in the form of CNN as well as the performance of each individual approach.…”

Section: Introductionmentioning

confidence: 99%

A review of convolutional neural network-based computer-aided lung nodule detection system

Sari

Sutikno²,

Soesanti³

et al. 2023

IJ-AI

View full text Add to dashboard Cite

Worldwide, lung cancer is the leading cause of mortality and rapidly spreads. Lung tissue that is benign does not grow significantly, but lung tissue that is malignant grows rapidly and attacks the body, posing a grave threat to one's health. This paper provides a literature review of computer-aided detection (CAD) systems for lung cancer diagnosis. Preprocessing, segmentation, detection, and classification are the stages of the CAD system. This review divides the preprocessing into three stages: image smoothing, edge sharpening, and noise removal. Additionally, lung segmentation is divided into three stages: histogram-based thresholding, linked component analysis, and lung extraction. The detecting phase aids in decreasing the workload. Several techniques are briefly described, including random forest, support vector machine (SVM), naive bayes, K-nearest neighbor (KNN), and convolutional neural network (CNN). Classification is the final stage; the image is then identified as containing or not possessing nodules. The prospect of incorporating CNN-based deep learning techniques into the CAD system is discussed. This paper is superior to other review studies on this topic due to its comprehensive examination of pertinent literature and structured presentation. We anticipate that our study may aid professional researchers and radiologists in developing more effective CAD systems for detecting lung cancer.

show abstract

A Comprehensive Review of Computer-Aided Diagnosis of Pulmonary Nodules Based on Computed Tomography Scans

Cited by 34 publications

References 88 publications

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Application of Recurrent Neural Network Algorithm in Intelligent Detection of Clinical Ultrasound Images of Human Lungs

A review of convolutional neural network-based computer-aided lung nodule detection system

Contact Info

Product

Resources

About