Study of Medical Image Processing Techniques Applied to Lung Cancer

Moreno, Silvia; Bonfante, Mario; Zurek, Eduardo; Juan, Homero San

doi:10.23919/cisti.2019.8760888

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…Medical imaging techniques, such as radiomics, can extract information from these images that are not available without computational tools and can be useful in the detection and treatment of cancer. Moreno et al [3] presents modern methods of image processing used in the study of lung cancer, with an emphasis on two main tasks: segmentation of nodes or tumors and extraction of useful features for classification and prediction of tumor evolution using Radiomics.…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of lung pathology using the fractal method

Abdikerimova,

Shekerbek,

Tulenbayev

et al. 2023

IJECE

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<span lang="EN-US">Currently, the detection of pathology of lung cavities and their digitalization is one of the urgent problems of the healthcare industry in Kazakhstan. In this paper, the method of fractal analysis was considered to solve the task set. Diagnosis of lung pathology based on fractal analysis is an actively developing area of medical research. Conducted experiments on a set of clinical data confirm the effectiveness of the proposed methodology. The results obtained show that fractal analysis can be a useful tool for early detection of lung pathologies. It allows you to detect even minor changes in the structure and texture of lung tissues, which may not be obvious during visual analysis. The article deals with images of pathology of the pulmonary cavity, taken from an open data source. Based on the analysis of fractal objects, they were pre-assembled. Software algorithms for the operation of the information system for screening diagnostics have been developed. Based on the information contained in the fractal image of the lungs, mathematical models have been developed to create a diagnostic rule. A reference set of information features has been created that allows you to create algorithms for diagnosing the lungs: healthy and with pathologies of tuberculosis. </span>

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Section: Literature Reviewmentioning

confidence: 99%

Detection of lung pathology using the fractal method

Abdikerimova,

Shekerbek,

Tulenbayev

et al. 2023

IJECE

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“…Medical image processing becomes familiar in the current decade thus medical imaging is the method for generating the visual images of an inside body part for medical investigation and medicinal interference. In addition, the medical imaging process contains disease classification and managing also it is utilized to gather the information of the inner organs [10], which involves organic and radiological imaging it can be used as electromagnetic, thermal, magnetic and isotope imaging [11]. Many methods are used for the confirmation of information available about the function of the body [12].…”

Section: Hybrid Optimization Algorithmsmentioning

confidence: 99%

Stratification of Cardiovascular Diseases Using Deep Learning

Doppala¹,

Bhattacharyya²,

Chakkravarthy³

2020

RIA

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Heart-based diseases are one of the causes for major death rate in the world. WHO (World Health Organization) specified that 17 million of people are losing their lives per year due to several heart diseases. Artificial Intelligence playing a prominent role in disease identification and prediction from medical data. Magnetic Resonance Imaging plays a vital role in producing detailed images of internal organs and soft tissues for better understanding the condition. Magnetic Resonance Image contains more noisy data this is one of the issues to be addressed, hence this research focuses on the prediction of cardiovascular diseases using an innovative hybrid algorithm and addresses the issue of noise using Hann filters. A Hybrid algorithm is proposed with combination of Cat Fuzzy Neural Model (CFuNM) and Hybrid Ant Colony and African Buffalo Optimization. Cat Fuzzy Neural Model (CFuNM) is used to classify cardiac diseases such as cardiomyopathy, pericardial effusion, coronary artery, amyloidosis, and other coronary heart diseases and for the severity analysis of disease we used Hybrid Ant Colony and African Buffalo Optimization (HAC-ABO) mechanism. This research of Hybrid deep learning model improved the classification accuracy of 99.3% and error rate of 0.18% which is considerably good when compared to existing methods.

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“…Lung cancer is one-fifth of the causes of death worldwide [1]. Lung cancer is generally detected at 55 to 70 years because diagnosing this cancer at an early stage is difficult.…”

Section: Introductionmentioning

confidence: 99%

“…The results of the medical image in the form of Computed Tomography (CT) provide the information we need to detect lung abnormalities [1]. However, the results of CT images often look blurry or lack contrast, so much important information is not read by analysts, causing different readings for the diagnosis and prognosis of this disease.…”

Section: Introductionmentioning

confidence: 99%

Gray Level Co-Occurrence Matrices and Support Vector Machine for Improved Lung Cancer Detection

Yunianto¹,

Suparmi

Cari

et al. 2023

Int. J. Onl. Eng.

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A detection system based on digital image processing and machine learning classification was developed to detect normal and cancerous lung conditions. 340 data from LIDC –IDRI were processed through several stages. The first stage is pre-processing using three filter variations and contrast stretching, which reduce noise and increase image contrast. The image segmentation process uses Otsu Thresholding to clarify the ROI of the image. The texture feature extraction with GLCM was applied using 21 feature variations. Data extraction is used as a label value learned by the classification system in the form of SVM. The results of the training data classification are processed with a confusion matrix which shows that the high pass filter has higher accuracy than the other two variations. The proposed method was assessed in terms of accuracy, precision and recall. The model provided an accuracy of 99.67 % training data and 97.50 % testing data.

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Study of Medical Image Processing Techniques Applied to Lung Cancer

Cited by 9 publications

References 26 publications

Detection of lung pathology using the fractal method

Detection of lung pathology using the fractal method

Stratification of Cardiovascular Diseases Using Deep Learning

Gray Level Co-Occurrence Matrices and Support Vector Machine for Improved Lung Cancer Detection

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