Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique

Foeady, Ahmad Zoebad; Riqmawatin, Siti Ria; Novitasari, Dian Candra Rini

doi:10.12928/telkomnika.v19i4.18874

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…These sickness stages can be accurately classified using deep learning convolution neural network methods. Several studies reviewed DLCNN such as [58], where its goal was to find the best CNN architecture for classifying lung carcinoma based on accuracy and training time calculations. Backpropagation (BP) is the best feed-forward neural network (FFNN) method, with an accuracy of 97.5 percent and training time of 12 s, and the kernel extreme learning machine (KELM) is the best feedback neural network (FBNN) method, with an accuracy of 97.5 percent and an 18 min 04 s training time.…”

Section: General Methodology Diagrammentioning

confidence: 99%

Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview

et al. 2023

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Lung auscultation has long been used as a valuable medical tool to assess respiratory health and has gotten a lot of attention in recent years, notably following the coronavirus epidemic. Lung auscultation is used to assess a patient’s respiratory role. Modern technological progress has guided the growth of computer-based respiratory speech investigation, a valuable tool for detecting lung abnormalities and diseases. Several recent studies have reviewed this important area, but none are specific to lung sound-based analysis with deep-learning architectures from one side and the provided information was not sufficient for a good understanding of these techniques. This paper gives a complete review of prior deep-learning-based architecture lung sound analysis. Deep-learning-based respiratory sound analysis articles are found in different databases including the Plos, ACM Digital Libraries, Elsevier, PubMed, MDPI, Springer, and IEEE. More than 160 publications were extracted and submitted for assessment. This paper discusses different trends in pathology/lung sound, the common features for classifying lung sounds, several considered datasets, classification methods, signal processing techniques, and some statistical information based on previous study findings. Finally, the assessment concludes with a discussion of potential future improvements and recommendations.

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Section: General Methodology Diagrammentioning

confidence: 99%

Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview

et al. 2023

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“…These sickness stages can be accurately classified using deep learning convolution neural network methods. Several studies reviewed DLCNN such as [51], where its goal was to find the best CNN architecture for classifying lung carcinoma based on accuracy and training time calculations. Backpropagation is the best feedforward neural network (FFNN) method, with an accuracy of 97.5 percent and training time of 12 s, and kernel extreme learning machine (KELM) is the best feedback neural network (FBNN) method, with an accuracy of 97.5 percent and an 18 min 04 s training time.…”

Section: Existing Solutionsmentioning

confidence: 99%

A review on lung disease recognition by acoustic signal analysis with deep learning networks

2023

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Recently, assistive explanations for difficulties in the health check area have been made viable thanks in considerable portion to technologies like deep learning and machine learning. Using auditory analysis and medical imaging, they also increase the predictive accuracy for prompt and early disease detection. Medical professionals are thankful for such technological support since it helps them manage further patients because of the shortage of skilled human resources. In addition to serious illnesses like lung cancer and respiratory diseases, the plurality of breathing difficulties is gradually rising and endangering society. Because early prediction and immediate treatment are crucial for respiratory disorders, chest X-rays and respiratory sound audio are proving to be quite helpful together. Compared to related review studies on lung disease classification/detection using deep learning algorithms, only two review studies based on signal analysis for lung disease diagnosis have been conducted in 2011 and 2018. This work provides a review of lung disease recognition with acoustic signal analysis with deep learning networks. We anticipate that physicians and researchers working with sound-signal-based machine learning will find this material beneficial.

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“…But, the model worked with ten anonymized H&E stained colorectal cancer (CRC) tissue slides. Meanwhile, Foeady et al [9], worked with Lung Cancer classifications using CT scans. Some research observes that the use of MRI considered bettering than CT scans.…”

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confidence: 99%

ADEMNET architecture: An innovative solution for adaptive multi-class balancing problem in image classification

Papanna Umalakshmi,

Sathyanarayana,

Chicktotlikere Nagappa

et al. 2024

IJEECS

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<span>In the field of medical image processing, achieving high performance in the classification of four types of dementia poses a significant challenge. This research presents a novel approach that outperforms existing methodologies, bringing about a transformative impact in this specialized domain. The method integrates the adaptive synthetic–nominal (ADASYN) technique with a DEMNET framework, resulting in a substantial performance improvement of 95.45% compared to current benchmarks. Through meticulous experimentation on a dementia dataset encompassing four distinct types, we consistently demonstrate significant enhancements achieved by the refined strategy. This innovation not only raises the performance standard but also provides a robust and adaptable solution that can be easily integrated into existing systems. The implications of this advancement open up new avenues for both research and practical applications. This work exemplifies the power of innovative approaches to push the limits of performance and establishes a new benchmark for excellence within this specific domain.</span>

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Lung cancer classification based on CT scan image by applying FCM segmentation and neural network technique

Cited by 7 publications

References 27 publications

Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview

Acoustic-Based Deep Learning Architectures for Lung Disease Diagnosis: A Comprehensive Overview

A review on lung disease recognition by acoustic signal analysis with deep learning networks

ADEMNET architecture: An innovative solution for adaptive multi-class balancing problem in image classification

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