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Automated diagnosis of Lungs Tumor Using Segmentation Techniques

S.Piramu Kailasam
2016 International Journal Of Engineering And Computer Science  
The Objective is to detect the cancerous lung nodules from 3D CT chest image and classify the lung disease and its severity.  ...  Here we used six feature extraction techniques such as bag of visual words based on the histogram oriented gradients, the wavelet transform based features, the local binary pattern, SIFT and Zernike moment  ...  Abdulla & Shaharun [12] used feed forward neural networks to classify lung nodules in X-ray images and with smaller feature of area, size and perimeter. Kuruvilla et al.  ... 
doi:10.18535/ijecs/v5i10.22 fatcat:cims2ydc6fesvgdaudo2c4phlq

Expert knowledge-infused deep learning for automatic lung nodule detection

Jiaxing Tan, Yumei Huo, Zhengrong Liang, Lihong Li
2019 Journal of X-Ray Science and Technology  
Computer aided detection (CADe) of pulmonary nodules from computed tomography (CT) is crucial for early diagnosis of lung cancer.  ...  However, the complexity of CT lung images renders a challenge of extracting effective features by self-learning only. This condition is exacerbated for limited size of datasets.  ...  Computed tomography (CT) imaging is the most popular imaging techniques for detecting and diagnosing the lung cancer, which typically consists of the task of detecting the pulmonary nodules on CT scans  ... 
doi:10.3233/xst-180426 pmid:30452432 pmcid:PMC6453714 fatcat:4fp6vw4k2jbzxmqelrmr7wiqam

Class dependency based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of Tuberculosis from chest x-rays [article]

G Jignesh Chowdary, Suganya G, Premalatha M, Karunamurthy K
2021 arXiv   pre-print
This is a two-step approach, where in the first step the lung regions are segmented from the chest x-rays using the graph cut method, and then in the second step the transfer learning of VGG16 combined  ...  So this paper presents an automatic approach for the diagnosis of TB from posteroanterior chest x-rays.  ...  Automated diagnosis of lung nodules using computerized methods is becoming a mature application of decision support for chest x-rays.  ... 
arXiv:2108.04329v1 fatcat:eiyootep6zgq7f5fq2hq2ikvfi

A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions

Stefanus Tao Hwa Kieu, Abdullah Bade, Mohd Hanafi Ahmad Hijazi, Hoshang Kolivand
2020 Journal of Imaging  
Hence, numerous work on the detection of lung disease using deep learning can be found in the literature. This paper presents a survey of deep learning for lung disease detection in medical images.  ...  The recent developments of deep learning support the identification and classification of lung diseases in medical images.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jimaging6120131 pmid:34460528 fatcat:jhi5r4nj5nccbklrdbtuk4qo6e

Lung Segmentation based Pulmonary Disease Classification using Deep Neural Networks

S. Zainab Yousuf Zaidi, M. Usman Akram, Amina Jameel, Norah Saleh Alghamdi
2021 IEEE Access  
on the entire chest x-ray images.  ...  FIGURE 6 . 6 The proposed custom CNN architecture with additional input and FC layers FIGURE 7 . 7 Segmentation Results (a) Chest x-ray Image, (b) The reference lung mask of the input chest x-ray utilized  ...  Her main areas of research are image processing, medical image processing, machine learning and computer vision. DR.  ... 
doi:10.1109/access.2021.3110904 fatcat:o52ech7majgapeksio6ir3emda

Automatic Tuberculosis Screening Using Chest Radiographs

Stefan Jaeger, Alexandros Karargyris, Sema Candemir, Les Folio, Jenifer Siegelman, Fiona Callaghan, Zhiyun Xue, Kannappan Palaniappan, Rahul K. Singh, Sameer Antani, George Thoma, Yi-Xiang Wang (+2 others)
2014 IEEE Transactions on Medical Imaging  
Index Terms-Computer-aided detection and diagnosis, lung, pattern recognition and classification, segmentation, tuberculosis (TB), X-ray imaging. 0278-0062  ...  For this lung region, we compute a set of texture and shape features, which enable the X-rays to be classified as normal or abnormal using a binary classifier.  ...  Qasba, Medical Director of Montgomery County's TB Control program, for providing the CXRs for the MC set.  ... 
doi:10.1109/tmi.2013.2284099 pmid:24108713 fatcat:k4llzzofljbixdxmx6luul5dfm

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Hanan Farhat, George E. Sakr, Rima Kilany
2020 Machine Vision and Applications  
This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19.  ...  It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.  ...  [230] also proposed a framework for COVID-19 detection using chest X-rays, starting with fuzzy color technique as a pre-processing step to restructure the data classes, then stack them with original  ... 
doi:10.1007/s00138-020-01101-5 pmid:32834523 pmcid:PMC7386599 fatcat:tkkylrptc5hkpoj52hjs3kuttu

Hybrid Learning of Hand-Crafted and Deep-Activated Features Using Particle Swarm Optimization and Optimized Support Vector Machine for Tuberculosis Screening

Khin Yadanar Win, Noppadol Maneerat, Kazuhiko Hamamoto, Syna Sreng
2020 Applied Sciences  
We describe a hybrid feature-learning algorithm for automatic screening of TB in chest x-rays: it first segmented the lung regions using the DeepLabv3+ model.  ...  Automated chest x-rays analysis can facilitate and expedite TB screening with fast and accurate reports of radiological findings and can rapidly screen large populations and alleviate a shortage of skilled  ...  Conflicts of Interest: We declare that there are no conflict of interest.  ... 
doi:10.3390/app10175749 fatcat:3s6vdxwpzjfcri6ldbz3xpijge

Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition

Sheeba Lal, Saeed Ur Rehman, Jamal Hussain Shah, Talha Meraj, Hafiz Tayyab Rauf, Robertas Damaševičius, Mazin Abed Mohammed, Karrar Hameed Abdulkareem
2021 Sensors  
We evaluate and analyze the adversarial attacks and defenses on the retinal fundus images for the Diabetic Retinopathy recognition problem, which is considered a state-of-the-art endeavor.  ...  Practical application in actual physical scenarios with adversarial threats shows their features.  ...  COVID-19 is diagnosed using Distant Domain Transfer Learning (DDTL) [30] . COVID-19 detection used fine-tuned convolutional neural networks and confined in chest X-ray images [31] .  ... 
doi:10.3390/s21113922 fatcat:ctlmaxj45bfdllzxclu7utc5we

Deep Feature Selection and Decision Level Fusion for Lungs Nodule Classification

Imdad Ali, Muhammad Muzammil, Ihsan Ul Haq, Amir A. Khaliq, Suheel Abdullah
2021 IEEE Access  
The existence of pulmonary nodules exhibits the presence of lung cancer. The Computer-Aided Diagnostic (CAD) and classification of such nodules in CT images lead to improve the lung cancer screening.  ...  In this work, we proposed a decision level fusion technique to improve the performance of the CAD system for lung nodule classification.  ...  For example, Wang et al. proposed a feature fusion technique for the classification of lung nodules in chest X-ray images by fusing the deep and handcraft features, such as; intensity, geometric and contrast  ... 
doi:10.1109/access.2021.3054735 fatcat:3tqttjje75bjvh4irroj4l2664

Abnormality detection and intelligent severity assessment of human chest computed tomography scans using deep learning: a case study on SARS-COV-2 assessment

Mohamed Ramzy Ibrahim, Sherin M. Youssef, Karma M. Fathalla
2021 Journal of Ambient Intelligence and Humanized Computing  
The infection with SARS-COV-2 virus shows an abnormality in lung parenchyma that can be effectively detected using Computed Tomography (CT) imaging.  ...  Different respiratory infections cause abnormal symptoms in lung parenchyma that show in chest computed tomography.  ...  Moreover, the accuracies of CT were shown to be higher than that of X-rays as in early stages of COVID-19, a chest X-ray may be identified as normal, while CT conveys early signs of the disease (Rony  ... 
doi:10.1007/s12652-021-03282-x pmid:34055098 pmcid:PMC8147594 fatcat:dx637yujpzfpnppiaoxqmgocei

Motion Prediction Model Using Adaptive Neuro Fuzzy Network (ANFN) and Probabilistic Neural Network (PNN) Algorithm in 4-Dimensional Computed Tomography (4DCT) Images

J. Vijayaraj, D. Loganathan
2020 International Journal of Current Research and Review  
Image processing techniques are widely employed to detect lung cancer earlier.  ...  Across the world, the major cause of death is due to lung cancer. Due to overlapping structure of cancer cell, earlier lung cancer detection is challenging.  ...  Jaber presents an automated intelligent method for nodule classification and detection.  ... 
doi:10.31782/ijcrr.2020.122328 fatcat:fbxef4zgm5h4zgstfacbk3574u

COVID-19 chest radiography employing via Deep Convolutional Stacked Autoencoders

Zhi-Hao Chen, Jyh-Ching Juang
2020 2020 International Automatic Control Conference (CACS)  
As a result, the proposed approach can quickly analyze X-ray images in detecting abnormalities in patients with lab-confirmed coronavirus even before clinical symptoms appear.  ...  The model improves the existing techniques used for CT images inspection through an application of Stacked Autoencoders (SAEs) structures using the segmentation function for the area object detection model  ...  learning with deep learning with the output of X-Ray images pneumonia lug use of a small amount of labeled data for supervised finetuning parameters.  ... 
doi:10.1109/cacs50047.2020.9289774 fatcat:7r3xrp2kgjeytnjqfojqngmhsq

A Review of Technological Progression from Radiomics to Breathomics for Early Detection of Lung Cancer

Funmilayo S. Moninuola, Oluwadamilola Oshin, Emmanuel Adetiba, Anthony A. Atayero, Ademola Adeyeye, Victoria Oguntosin, Olushola O. James, Anthony A. Adegoke, Obiseye O. Obiyemi, Surendra Thakur, Abdultaofeek Abayomi
2021 Journal of Computer Science  
Studies on the potential use of E-nose as an easy and non-invasive tool to analyze VOCs in the exhaled breath of lung cancer patients to characterize suspected lung nodules are also presented.  ...  Breath analysis is an ideal approach for the detection of metabolites relating to cancer cells in the lungs.  ...  KZN e-Skills CoLab, Durban University of Technology, Durban, South Africa provided support for the publication of this work.  ... 
doi:10.3844/jcssp.2021.1071.1084 fatcat:iuxuyuwbk5d4ha5ee26via5atu

Deep learning in generating radiology reports: A survey

Maram Mahmoud A. Monshi, Josiah Poon, Vera Chung
2020 Artificial Intelligence in Medicine  
Substantial progress has been made towards implementing automated radiology reporting models based on deep learning (DL). This is due to the introduction of large medical text/image datasets.  ...  Generating radiology coherent paragraphs that do more than traditional medical image annotation, or single sentence-based description, has been the subject of recent academic attention.  ...  Figure 1 shows an example in the form of an IU X-ray [21] dataset. Here, each report is associated with two chest X-ray images.  ... 
doi:10.1016/j.artmed.2020.101878 pmid:32425358 pmcid:PMC7227610 fatcat:ccy2g2rh2zavdjjvvjlv7poxau
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