Lung cancer segmentation and diagnosis of lung cancer staging using MEM (modified expectation maximization) algorithm and artificial neural network fuzzy inference system (ANFIS)

Lavanya, M.; P, Muthu Kannan

doi:10.4066/biomedicalresearch.29-18-740

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…However, the systems need preprocessing data for ANFIS modelling. Image processing of computer tomography (CT) images of chest were applied for lung cancer staging [19], lung cancer recognition [20], pulmonary TB diagnosis [21] and Chronic Obstructive Pulmonary Disease (COPD) screening [22]. Besides, those designed systems are still put in an application for one type of disease.…”

Section: Introductionmentioning

confidence: 99%

An application of ANFIS for Lung Diseases Early Detection System

Santoso

Disrinama

Amrullah

2020

KINETIK

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Indonesian Basic Health Research in 2018 showed the prevalence of pneumonia, pulmonary tuberculosis (TB) and lung cancer in Indonesia 4.0% 0.4% and 0.18%, respectively. However, the number of lung specialists is small. According to the Indonesian Lung Specialist Association webpage, the number of doctors joined in the association up to 2008 were 452. This amount is very less when compared with existing lung disease cases. Thus, the handling of lung disease will be too late. The use of ANFIS for early detection of lung disease is growing. However, the systems designed are need preprocessing data to be executed and still applied for one type of disease. This paper will design a desktop application based on ANFIS expert system to detect lung disease early, i.e. for pneumonia, pulmonary TB and lung cancer. The system will work based on simple symptoms expressed by the patient. Subtractive clustering is used for clustering process. The results of the training showed that the models were able to give better performance compared to the model built using conventional clustering methods. The test results show that those three models have comparable performance compared to their counterpart. Software validation shows that the it gives 94.00% succeed for training data and up to 100% for testing data. This application is not intended to replace the role of a doctor, but to help diagnose the patient's condition earlier.

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Section: Introductionmentioning

confidence: 99%

An application of ANFIS for Lung Diseases Early Detection System

Santoso

Disrinama

Amrullah

2020

KINETIK

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ANFIS-Net for automatic detection of COVID-19

Al-Ali

Elharrouss

Qidwai

et al. 2021

Sci Rep

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Among the most leading causes of mortality across the globe are infectious diseases which have cost tremendous lives with the latest being coronavirus (COVID-19) that has become the most recent challenging issue. The extreme nature of this infectious virus and its ability to spread without control has made it mandatory to find an efficient auto-diagnosis system to assist the people who work in touch with the patients. As fuzzy logic is considered a powerful technique for modeling vagueness in medical practice, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was proposed in this paper as a key rule for automatic COVID-19 detection from chest X-ray images based on the characteristics derived by texture analysis using gray level co-occurrence matrix (GLCM) technique. Unlike the proposed method, especially deep learning-based approaches, the proposed ANFIS-based method can work on small datasets. The results were promising performance accuracy, and compared with the other state-of-the-art techniques, the proposed method gives the same performance as the deep learning with complex architectures using many backbone.

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Improved graph neural network-based green anaconda optimization for segmenting and classifying the lung cancer

Dinesh Krishnan,

Pelusi,

Daniel

et al. 2023

MBE

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<abstract> <p>Normal lung cells incur genetic damage over time, which causes unchecked cell growth and ultimately leads to lung cancer. Nearly 85% of lung cancer cases are caused by smoking, but there exists factual evidence that beta-carotene supplements and arsenic in water may raise the risk of developing the illness. Asbestos, polycyclic aromatic hydrocarbons, arsenic, radon gas, nickel, chromium and hereditary factors represent various lung cancer-causing agents. Therefore, deep learning approaches are employed to quicken the crucial procedure of diagnosing lung cancer. The effectiveness of these methods has increased when used to examine cancer histopathology slides. Initially, the data is gathered from the standard benchmark dataset. Further, the pre-processing of the collected images is accomplished using the Gabor filter method. The segmentation of these pre-processed images is done through the modified expectation maximization (MEM) algorithm method. Next, using the histogram of oriented gradient (HOG) scheme, the features are extracted from these segmented images. Finally, the classification of lung cancer is performed by the improved graph neural network (IGNN), where the parameter optimization of graph neural network (GNN) is done by the green anaconda optimization (GAO) algorithm in order to derive the accuracy maximization as the major objective function. This IGNN classifies lung cancer into normal, adeno carcinoma and squamous cell carcinoma as the final output. On comparison with existing methods with respect to distinct performance measures, the simulation findings reveal the betterment of the introduced method.</p> </abstract>

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Lung cancer segmentation and diagnosis of lung cancer staging using MEM (modified expectation maximization) algorithm and artificial neural network fuzzy inference system (ANFIS)

Cited by 3 publications

References 21 publications

An application of ANFIS for Lung Diseases Early Detection System

An application of ANFIS for Lung Diseases Early Detection System

ANFIS-Net for automatic detection of COVID-19

Improved graph neural network-based green anaconda optimization for segmenting and classifying the lung cancer

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