Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest

Paing, May Phu; Hamamoto, Kazuhiko; Tungjitkusolmun, S.; Visitsattapongse, Sarinporn; Pintavirooj, C.

doi:10.3390/app10072346

Cited by 10 publications

(9 citation statements)

References 43 publications

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“…Many approaches have integrated these characteristics. Paing et al [ 91 ] developed a fully automated and optimized random forest approach to classifying pulmonary nodules using tomography scans. A 3D chain code algorithm is applied to improve the borders.…”

Section: Lung Cancermentioning

confidence: 99%

State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

Ali

Pei

et al. 2021

Cancers

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Thus far, the most common cause of death in the world is cancer. It consists of abnormally expanding areas that are threatening to human survival. Hence, the timely detection of cancer is important to expanding the survival rate of patients. In this survey, we analyze the state-of-the-art approaches for multi-organ cancer detection, segmentation, and classification. This article promptly reviews the present-day works in the breast, brain, lung, and skin cancer domain. Afterwards, we analytically compared the existing approaches to provide insight into the ongoing trends and future challenges. This review also provides an objective description of widely employed imaging techniques, imaging modality, gold standard database, and related literature on each cancer in 2016–2021. The main goal is to systematically examine the cancer diagnosis systems for multi-organs of the human body as mentioned. Our critical survey analysis reveals that greater than 70% of deep learning researchers attain promising results with CNN-based approaches for the early diagnosis of multi-organ cancer. This survey includes the extensive discussion part along with current research challenges, possible solutions, and prospects. This research will endow novice researchers with valuable information to deepen their knowledge and also provide the room to develop new robust computer-aid diagnosis systems, which assist health professionals in bridging the gap between rapid diagnosis and treatment planning for cancer patients.

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Section: Lung Cancermentioning

confidence: 99%

State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

Ali

Pei

et al. 2021

Cancers

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“…Detection of a lung nodule, segmentation and classification only based on simple morphological and textural properties such as size or texture or shape features is not robust as suggested by Paing et al [38] and does not reveal the exact magnitude of the underlying challenges in lung cancer detection and diagnosis. Researchers have used various techniques for lung nodule detection and segmentation such as 3D tensor filtering with local image feature analysis [18], global optimal active contour model [54], corner seeded region growing combined with differential evolution based optimal thresholding [35], connected component labelling with morphological operations and multilayer perceptron [21], sparse field level sets and boosting algorithm [39], adaptive ROI with multi-view residual learning [49], LBF active contour model with information entropy and joint vector [22] etc.…”

Section: Challenges To Lung Cancer Detectionmentioning

confidence: 99%

Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images

et al. 2020

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Lung nodule segmentation is an essential step in any CAD system for lung cancer detection and diagnosis. Traditional approaches for image segmentation are mainly morphology based or intensity based. Motion-based segmentation techniques tend to use the temporal information along with the morphology and intensity information to perform segmentation of regions of interest in videos. CT scans comprise of a sequence of dicom 2-D image slices similar to videos which also comprise of a sequence of image frames ordered on a timeline. In this work, Farneback, Horn-Schunck and Lucas-Kanade optical flow methods have been used for processing the dicom slices. The novelty of this work lies in the usage of optical flow methods, generally used in motion-based segmentation tasks, for the segmentation of nodules from CT images. Since thin-sliced CT scans are the imaging modality considered, they closely approximate the motion videos and are the primary motivation for using optical flow for lung nodule segmentation. This paper also provides a detailed comparative analysis and validates the effectiveness of using optical flow methods for segmentation. Finally, we propose methods to further improve the efficiency of segmentation using optical flow methods on CT scans.

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“…Some improved versions of ELM gain deep structures by adding an auto-encoder to construct or stack hidden layers, which can perform feature learning. Random Forest is a derivative algorithm based on a classification tree [20], which is classified as machine learning due to the need for simulation and iteration. As a new and highly flexible algorithm, Random Forest has a wide range of application prospects, from marketing to infectious disease research.…”

Section: Introductionmentioning

confidence: 99%

A Solution for Liquor Recognition Based on PCA-RF and Laser Induced Fluorescence

Song

Huang

2021

IEEE Access

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There are many brands and types of liquor in China. Yet, some merchants use low-quality liquor for high-quality sales, which contains methanol that is harmful to humans. Therefore, the identification of fake liquor is of great significance to human health and brand protection. This paper presents a laser-induced fluorescence (which has very high sensitivity and rapidity) recognition liquor. Specifically, it first sets up a LIF spectrum acquisition system for liquor in the laboratory, uses Principal Component Analysis (PCA) to reduce the dimension of LIF spectral data, and then collects the dimension reduction data for Random Forest (RF) identification. The results show that the average recognition accuracy of RF combined with PCA for liquor is as high as 99.80%. This paper combines PCA-RF with LIF to accurately identify different brands and types of liquor, which provides a new method for the recognition of fake liquor.

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Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest

Cited by 10 publications

References 43 publications

State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images

A Solution for Liquor Recognition Based on PCA-RF and Laser Induced Fluorescence

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