Automatic lung segmentation in chest radiographs using shadow filter and local thresholding

Pattrapisetwong, Preeyanan; Chiracharit, Werapon

doi:10.1109/cibcb.2016.7758113

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…The advent o ANNs, fueled by the power of deep learning, has paved the way for more precise and automated analysis of medical images. CNNs, a subset of ANNs, excel in capturing intri cate patterns and features within images, enabling them to identify subtle anomalies in dicative of various lung diseases as Figure 2 [6][7][8][9][10][11]. The cornerstone of the proposed system lies in its ability to learn complex represen tations from labeled datasets containing a diverse array of lung images.…”

Section: Introductionmentioning

confidence: 99%

Development of an Artificial Neural Network-Based Image Retrieval System for Lung Disease Classification and Identification

Singh,

Kumar

et al. 2024

Cc 2023

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Section: Introductionmentioning

confidence: 99%

Development of an Artificial Neural Network-Based Image Retrieval System for Lung Disease Classification and Identification

Singh,

Kumar

et al. 2024

Cc 2023

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“…By avoiding the multiplication effect in gradient backpropagation, the network is able to obtain accurate data on the Lung field without experiencing any instability. On the basis of shadow filter and local thresholding [7], an unsupervised learning method for separating the lungs from chest x-rays was created. Metrics for how well the proposed method works are higher than 90%.…”

Section: Introductionmentioning

confidence: 99%

Lung Segmentation of Chest Radiograph Using Circular Window Based Local Contrast Thresholding (CWLCT) and Adaptive Median Outlier (CWAMO)

Kanade,

Helonde,

Burade

et al. 2023

ITM Web Conf.

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Certain chest illnesses, such as TB, adenocarcinoma, squamous cell carcinoma, large cell carcinoma, atelectasis, etc., can be diagnosed in chest radiographs, and the development of a CAD system relies in part on accurate lung segmentation. In order to partition the lungs in chest radiographs, this work introduces an unsupervised learning approach based on a circular window and local thresholding. The procedure involves pre-processing, a preliminary estimate of the lung field, and the elimination of noise. Images are initially scaled down to 1024x1024 and enhanced using adaptive histogram equalization. Then chest radiographs are binarized using the proposed method. Based on the geometrical and special characteristics, lungs are separated from the chest radiographs. The final step in picture segmentation is the use of morphological processes. Local thresholding, omitting extraneous body parts, filling in gaps, and filtering regions based on their attributes all contribute to preliminary estimates of the lung field. Morphological processes are used as a means of eliminating background noise. A public bone shadow eliminated JSRT dataset consisting of 247 chest x-rays is used to measure the performance of the proposed method. The effectiveness of the proposed method results’ performance is evaluated by comparing it with Active Shape Model (ASM) based lung segmentation for various performance metrics such as F-score, overlap percentage, accuracy rate, sensitivity, specificity, and precision rates. All the parameters for the proposed method are over and above 90%. Our investigations indicate that the suggested method is an unsupervised learning approach that does not require any training.

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“…It led to the death of around 808,694 children having age less than 5 years in 2017, which constituted about 15% of the total deaths of children of the same age group [1]. Many computer aided tools have been invented to analyze the radiographic image of the chest [2]. Delay in diagnosis and adequate treatment are considered to be the major reasons accounting for the high rate of pneumonia among young age people.…”

Section: Introductionmentioning

confidence: 99%

Pneumonia Detection using Depth-Wise Convolutional Neural Network (DW-CNN)

Walia¹,

Srivastava²,

Kumar³

et al. 2020

EAI Endorsed Trans Perv Health Tech

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Pneumonia is most significant disease in today's world. It resulted around 15 % of the total deaths of children of the same age group. OBJECTIVES: This paper proposes Depth Wise Convolution Neural Network (DW-CNN) using the SWISH Activation and Transfer Learning (VGG16) to reliably diagnose pneumonia. METHODS: The proposed model contains 10 layers of convolutional neural networks. Also, three dense layers with the Swish activation function with a dropout of 0.7 and 0.5 respectively in each layer. The model was trained on 5216 augmented with weighted contrast and brightened radiograph Images and tested on 624 radiogram images using Deep Learning and Transfer Learning (VGG16). RESULT: The model was trained on 5216 augmented radiograph Images and tested on 624 radiogram images using Deep Learning and Transfer Learning (VGG16) and the final results obtained with training accuracy of 98.5%, testing accuracy of 79.8% and validation accuracy of 75%. CONCLUSION: The model can be improved by using different transfer learning models and hyperparameter tuning parameters.

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Automatic lung segmentation in chest radiographs using shadow filter and local thresholding

Cited by 9 publications

References 23 publications

Development of an Artificial Neural Network-Based Image Retrieval System for Lung Disease Classification and Identification

Development of an Artificial Neural Network-Based Image Retrieval System for Lung Disease Classification and Identification

Lung Segmentation of Chest Radiograph Using Circular Window Based Local Contrast Thresholding (CWLCT) and Adaptive Median Outlier (CWAMO)

Pneumonia Detection using Depth-Wise Convolutional Neural Network (DW-CNN)

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