MSANet: Multiscale Aggregation Network Integrating Spatial and Channel Information for Lung Nodule Detection

Guo, Zhitao; Zhao, Lidan; Yuan, Jinli; Yu, Hengyong

doi:10.1109/jbhi.2021.3131671

Cited by 30 publications

(11 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly Cao et al [22] has shown better performance for larger values of false positive rate by using two stage strategy. Guo et al [48] has obtain lesser CPM and its performance degrades for smaller value of false positives. Similarly, Li et al [30], Zhao et al [24], and Mei et al [29] obtained lower performance than the proposed scheme.…”

Section: Comparison With the Previous Studiesmentioning

confidence: 99%

MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection

Usman¹,

Rehman²,

Shahid³

et al. 2022

Preprint

View full text Add to dashboard Cite

Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the heterogeneity of the lung nodules and the complexity of the surrounding environment, it is a challenge to develop a robust nodule detection method. Numerous efforts have been made to develop an efficient Computer-aided detection (CADe) systems, albeit none compliance with the routine workflow of radiologists which limits the adaptability of such CADe system. To overcome this deficiency, in this study we propose a lung nodule detection scheme which fully incorporates the clinic workflow of radiologists. Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i.e., 3, 5 and 10mm) along with 3D patch of CT scan, consisting of 10 adjacent slices to feed into self distillation based Multi-Encoders Network (MEDS-Net). The propose architecture first condense 3D patch input to three channels by using dense block which consists of dense units which effectively examines the nodule presence from 2D axial slices. This condensed information, along with the forward and backward MIP images is fed to three different encoders to learn the most meaningful representation which is forwarded into decoded block at various levels. At decoder block, we employ self distillation mechanism by connecting the distillation block which contains five lung nodule detectors. It helps to expedite the convergence and improves the learning ability of proposed architecture. Finally, the propose scheme reduces the false positives by complementing the main detector with auxiliary detectors. The proposed scheme has been rigorously evaluated on 888 scans of LUNA16 dataset and obtained a CPM score of 93.6%. The results demonstrate that incorporating of bi-direction MIP images enables MEDS-Net to effectively distinguish nodules from surroundings which helps to achieve the sensitivity of . false positives per scans with the sensitivity of 91.5% and 92.8% with the false positive rate of 0.25 and 0.5 per scan, respectively.

show abstract

Section: Comparison With the Previous Studiesmentioning

confidence: 99%

MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection

Usman¹,

Rehman²,

Shahid³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…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

“…A method for fully automated segmentation of several types of lung nodules and generation of intra-nodular heterogeneity images for clinical usage has been proposed in Song et al (2022). For 3D pulmonary nodule detection, Guo et al (2022) suggested a multiscale aggregation network that uses both spatial and channel information. In Majumder et al (2022), essential genes are extracted using rough set theory, and probable gene triplets are generated using a fuzzy inference method.…”

Section: Related Workmentioning

confidence: 99%

A hybrid learning method for distinguishing lung adenocarcinoma and squamous cell carcinoma

Swain

Swetapadma

Rout

et al. 2023

DTA

View full text Add to dashboard Cite

PurposeThe objective of the proposed work is to identify the most commonly occurring non–small cell carcinoma types, such as adenocarcinoma and squamous cell carcinoma, within the human population. Another objective of the work is to reduce the false positive rate during the classification.Design/methodology/approachIn this work, a hybrid method using convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) and long-short-term memory networks (LSTMs) has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. To extract features from non–small cell lung carcinoma images, a three-layer convolution and three-layer max-pooling-based CNN is used. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types. The accuracy of the proposed method is 99.57 per cent, and the false positive rate is 0.427 per cent.FindingsThe proposed CNN–XGBoost–LSTM hybrid method has significantly improved the results in distinguishing between adenocarcinoma and squamous cell carcinoma. The importance of the method can be outlined as follows: It has a very low false positive rate of 0.427 per cent. It has very high accuracy, i.e. 99.57 per cent. CNN-based features are providing accurate results in classifying lung carcinoma. It has the potential to serve as an assisting aid for doctors.Practical implicationsIt can be used by doctors as a secondary tool for the analysis of non–small cell lung cancers.Social implicationsIt can help rural doctors by sending the patients to specialized doctors for more analysis of lung cancer.Originality/valueIn this work, a hybrid method using CNN, XGBoost and LSTM has been proposed to distinguish between lung adenocarcinoma and squamous cell carcinoma. A three-layer convolution and three-layer max-pooling-based CNN is used to extract features from the non–small cell lung carcinoma images. A few important features have been selected from the extracted features using the XGBoost algorithm as the optimal feature. Finally, LSTM has been used for the classification of carcinoma types.

show abstract

MSANet: Multiscale Aggregation Network Integrating Spatial and Channel Information for Lung Nodule Detection

Cited by 30 publications

References 33 publications

MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection

MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection

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

A hybrid learning method for distinguishing lung adenocarcinoma and squamous cell carcinoma

Contact Info

Product

Resources

About