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medical-6

Lung Nodule Classification

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Dataset

Best Model

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LIDC-IDRI

Libraries

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Datasets

LIDC-IDRI

Subtasks

Lung Nodule 3D Classification

Most implemented papers

DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification

Xiaohui Xie, Wentao Zhu, Chaochun Liu, Wei Fan•Wed Jan 24 2018

DeepLung has performance comparable to experienced doctors both for the nodule-level and patient-level diagnosis on the LIDC-IDRI dataset and surpassed the performance of experienced doctors based on image modality.

417

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Introduction Benchmarks Datasets Subtasks Libraries Papers

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Diagnostic classification of lung nodules using 3D neural networks

Raunak Dey, Zhongjie Lu, Yi Hong•Sun Mar 18 2018

The 3D multi-output DenseNet (MoDenseNet) achieves the state-of-the-art classification accuracy on the task of end-to-end lung nodule diagnosis and can be further extended to handle smaller datasets using transfer learning.

155 0

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Fast CapsNet for Lung Cancer Screening

Aryan Mobiny, H. Nguyen•Mon Jun 18 2018

It is shown that CapsNets significantly outperforms CNNs when the number of training samples is small, and an efficient alternative, called convolutional decoder, is proposed that yields lower reconstruction error and higher classification accuracy.

144 0

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Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimization

S. Ling, Miao Zhang, Huiqi Li, Juan Lyu, S. Su•Tue Jan 01 2019

A non-stationary kernel is proposed which allows the surrogate model to adapt to functions whose smoothness varies with the spatial location of inputs, and a multi-level convolutional neural network is built for lung nodule classification whose hyperparameter configuration is optimized by using the proposed non- Stationary kernel based Gaussian surrogate model.

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Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning

Ivan William Harsono, Suryadiputra Liawatimena, T. W. Cenggoro•Tue Mar 31 2020

I3DR-Net successfully outperform previous state-of-the-art Retina U-Net and U-FRCNN + mean average precision (mAP) by 7.9% and 7.2% for malignant nodule detection and classification task.

103 0

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Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification

Hanliang Jiang, Fuhao Shen, Fei Gao, Weidong Han•Mon Jan 18 2021

This work uses Emph{neural architecture search} (NAS) to automatically search 3D network architectures with excellent accuracy/speed trade-off and uses the convolutional block attention module (CBAM) in the networks, which helps to understand the reasoning process.

95 0

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Lung cancer detection from thoracic CT scans using an ensemble of deep learning models

Nandita Gautam, Abhishek Basu, Ram Sarkar•Mon Nov 20 2023

A new ensemble of deep learning models to accurately classify the severity of lung nodules is proposed, which achieves an accuracy of 97.23%, surpassing various recent methods and outperforming commonly used ensemble techniques.

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Lung Nodule Classification | State-of-the-Art