Automatic lung parenchyma segmentation using a deep convolutional neural network from chest X-rays

Maity, Arunit; Nair, T. R. Gopalakrishnan; Mehta, Shaanvi; Periasamy, P.

doi:10.1016/j.bspc.2021.103398

Cited by 17 publications

(8 citation statements)

References 29 publications

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“…With recent advancements in deep learning, CNN-based methods have also been developed for this task. Souza et al [16] and Maity et al [13] proposed two different segmentation models for lung segmentation. Their models were trained and tested on the MC [22], and JSRT datasets [23].…”

Section: A Lung Segmentationmentioning

confidence: 99%

A Dual-Branch Network for Diagnosis of Thorax Diseases From Chest X-Rays

Chowdary

Kanhangad

2022

IEEE J. Biomed. Health Inform.

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Section: A Lung Segmentationmentioning

confidence: 99%

A Dual-Branch Network for Diagnosis of Thorax Diseases From Chest X-Rays

Chowdary

Kanhangad

2022

IEEE J. Biomed. Health Inform.

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“…Image processing techniques or applications are utilised in many domains in military manufacturing, machine vision, monitoring and tracking (vehicle tracking) and many more [1][2][3][4][5][6]. Moreover, the medical sector is one of the sectors in which image processing techniques are utilised, such as chest x-rays, magnetic brain imaging, etc [7][8][9][10][11]. These techniques are utilised to eliminate the risk of misdiagnosis, which may lead to the death of the patient [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

The Distinction between R-CNN and Fast R-CNN in Image Analysis: A Performance Comparison

Mijwil

Aggarwal

Doshi

et al. 2022

AJAS

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Deep learning techniques have become vital in many fields in the modern era because they are excellent at analysing and predicting real big data to act in different situations. Although it is marvellous in many aspects, it is prone to misinterpretation of data, so teams of experienced specialists cannot be dispensed with in following up on the execution stages of data analysis. Convolutional Neural Network is one of the most significant deep learning techniques. It is widely employed in visual image analysis. In this article, R-CNN and Fast R-CNN are summarised and compared and are the best in image analysis. This article concluded that the most suitable performance is for Fast R-CNN in testing and training.

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“…Liu and Wei [ 11 ] segmented lung parenchyma based on matrix grey incident. Maity et al [ 12 ] established CNN network to segment lung parenchyma.…”

Section: Introductionmentioning

confidence: 99%

Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net

Zhang¹,

Lin²,

Hui³

et al. 2022

Computational and Mathematical Methods in Medicine

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Lung nodules are the main lesions of the lung, and conditions of the lung can be directly displayed through CT images. Due to the limited pixel number of lung nodules in the lung, doctors have the risk of missed detection and false detection in the detection process. In order to reduce doctors’ work intensity and assist doctors to make accurate diagnosis, a lung nodule segmentation and recognition algorithm is proposed by simulating doctors’ diagnosis process with computer intelligent methods. Firstly, the attention mechanism model is established to focus on the region of lung parenchyma. Then, a pyramid network of bidirectional enhancement features is established from multiple body positions to extract lung nodules. Finally, the morphological and imaging features of lung nodules are calculated, and then, the signs of lung nodules can be identified. The experiments show that the algorithm conforms to the doctor’s diagnosis process, focuses the region of interest step by step, and achieves good results in lung nodule segmentation and recognition.

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Automatic lung parenchyma segmentation using a deep convolutional neural network from chest X-rays

Cited by 17 publications

References 29 publications

A Dual-Branch Network for Diagnosis of Thorax Diseases From Chest X-Rays

A Dual-Branch Network for Diagnosis of Thorax Diseases From Chest X-Rays

The Distinction between R-CNN and Fast R-CNN in Image Analysis: A Performance Comparison

Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net

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