Analysis of Architecture Combining Convolutional Neural Network (CNN) and Kernel K-Means Clustering for Lung Cancer Diagnosis

Rustam, Zuherman; Hartini, Sri; Pratama, Rivan Y.; Yunus, Reyhan Eddy; Hidayat, Rahmat

doi:10.18517/ijaseit.10.3.12113

Cited by 23 publications

(13 citation statements)

References 34 publications

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“…For example, PCA and the k-means procedure might be used as a first step to obtain training data, before applying CNN. Similar approaches have already been successfully applied in other fields of research (e.g., Tang et al 2017;Rustam et al 2020). A disadvantage of machine learning approaches is, however, that the algorithms are somewhat like "black boxes" and as such might lead to misinterpretation by the user.…”

Section: Supervised Machine Learningmentioning

confidence: 96%

A review of statistical tools for morphometric analysis of juvenile pyroclasts

et al. 2021

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Morphometric analyses are based on multiparametric datasets that describe quantitatively the shapes of objects. The stochastic nature of fracture-formation processes that break up magma during explosive eruptions yields mixtures of particles that have highly varied shapes. In volcanology morphometric analysis is applied to these mixtures of particles with diverse shapes for two purposes: (1) to fingerprint tephra from individual eruptions and use the fingerprints to distinguish among tephra layers and determine their extents, and (2) to reconstruct eruption processes, by linking particles formed by known fragmentation processes in experiments with particles from natural pyroclastic deposits. Here we review the most commonly adopted statistical techniques for morphometric analysis of pyroclasts. We provide sets of objects with different shapes, along with their morphometric data, in order to demonstrate and illustrate the methods. They can be used not only for addressing the processes of fragmentation during explosive eruptions, but also for the characterization of other types of solid particles with complex morphologies.

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Section: Supervised Machine Learningmentioning

confidence: 96%

A review of statistical tools for morphometric analysis of juvenile pyroclasts

et al. 2021

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“…This article describes the results of studies on the use of biosensing in the treatment of lung cancer. According to the study, the electrochemical biosensor is the most widely utilized for early-stage lung cancer diagnosis [ 30 ].…”

Section: Related Workmentioning

confidence: 99%

Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer

Ali

Chooralil²,

Balasubramanian

et al. 2023

Bioengineering

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Recently, deep learning and the Internet of Things (IoT) have been widely used in the healthcare monitoring system for decision making. Disease prediction is one of the emerging applications in current practices. In the method described in this paper, lung cancer prediction is implemented using deep learning and IoT, which is a challenging task in computer-aided diagnosis (CAD). Because lung cancer is a dangerous medical disease that must be identified at a higher detection rate, disease-related information is obtained from IoT medical devices and transmitted to the server. The medical data are then processed and classified into two categories, benign and malignant, using a multi-layer CNN (ML-CNN) model. In addition, a particle swarm optimization method is used to improve the learning ability (loss and accuracy). This step uses medical data (CT scan and sensor information) based on the Internet of Medical Things (IoMT). For this purpose, sensor information and image information from IoMT devices and sensors are gathered, and then classification actions are taken. The performance of the proposed technique is compared with well-known existing methods, such as the Support Vector Machine (SVM), probabilistic neural network (PNN), and conventional CNN, in terms of accuracy, precision, sensitivity, specificity, F-score, and computation time. For this purpose, two lung datasets were tested to evaluate the performance: Lung Image Database Consortium (LIDC) and Linear Imaging and Self-Scanning Sensor (LISS) datasets. Compared to alternative methods, the trial outcomes showed that the suggested technique has the potential to help the radiologist make an accurate and efficient early lung cancer diagnosis. The performance of the proposed ML-CNN was analyzed using Python, where the accuracy (2.5–10.5%) was high when compared to the number of instances, precision (2.3–9.5%) was high when compared to the number of instances, sensitivity (2.4–12.5%) was high when compared to several instances, the F-score (2–30%) was high when compared to the number of cases, the error rate (0.7–11.5%) was low compared to the number of cases, and the computation time (170 ms to 400 ms) was low compared to how many cases were computed for the proposed work, including previous known methods. The proposed ML-CNN architecture shows that this technique outperforms previous works.

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“…However, the k-means algorithm was more widely used in some cancer aspects. Rustam et al [ 12 ] applied this technique to obtain the centroid of each cluster and predict the class of every data point in the validation set. Recently, Ronen et al [ 13 ] used k-means as an initial step in a deep learning method to evaluate the colorectal cancer subtypes.…”

Section: Introductionmentioning

confidence: 99%

Use of Multiple Correspondence Analysis and K-means to Explore Associations Between Risk Factors and Likelihood of Colorectal Cancer: Cross-sectional Study

Florensa¹,

Mateo²,

Solsona³

et al. 2022

J Med Internet Res

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Background Previous works have shown that risk factors are associated with an increased likelihood of colorectal cancer. Objective The purpose of this study was to detect these associations in the region of Lleida (Catalonia) by using multiple correspondence analysis (MCA) and k-means. Methods This cross-sectional study was made up of 1083 colorectal cancer episodes between 2012 and 2015, extracted from the population-based cancer registry for the province of Lleida (Spain), the Primary Care Centers database, and the Catalan Health Service Register. The data set included risk factors such as smoking and BMI as well as sociodemographic information and tumor details. The relations between the risk factors and patient characteristics were identified using MCA and k-means. Results The combination of these techniques helps to detect clusters of patients with similar risk factors. Risk of death is associated with being elderly and obesity or being overweight. Stage III cancer is associated with people aged ≥65 years and rural/semiurban populations, while younger people were associated with stage 0. Conclusions MCA and k-means were significantly useful for detecting associations between risk factors and patient characteristics. These techniques have proven to be effective tools for analyzing the incidence of some factors in colorectal cancer. The outcomes obtained help corroborate suspected trends and stimulate the use of these techniques for finding the association of risk factors with the incidence of other cancers.

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Analysis of Architecture Combining Convolutional Neural Network (CNN) and Kernel K-Means Clustering for Lung Cancer Diagnosis

Cited by 23 publications

References 34 publications

A review of statistical tools for morphometric analysis of juvenile pyroclasts

A review of statistical tools for morphometric analysis of juvenile pyroclasts

Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer

Use of Multiple Correspondence Analysis and K-means to Explore Associations Between Risk Factors and Likelihood of Colorectal Cancer: Cross-sectional Study

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