Optimized Deformable Model-based Segmentation and Deep Learning for Lung Cancer Classification

Shetty, Mamtha V; Jayadevappa, D.; Tunga, Satish

doi:10.2152/jmi.69.244

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…For example, ref. [33] using CNN-ResNet50 on LIDC achieved an accuracy of 88.41% while [36] with SchCNN achieved an accuracy of 93.03%, illustrating the effectiveness of specific optimization methods despite the use of the same dataset. Data quantity also plays a crucial role in model performance.…”

Section: Discussionmentioning

confidence: 99%

“…Shetty et al [36] presented a new technique for accurate segmentation and classification of lung cancer using CT images by applying optimized deformable models and deep learning techniques. The proposed method involved pre-processing, lung lobe segmentation, lung cancer segmentation, data augmentation, and lung cancer classification.…”

Section: Deep Learning Techniques For Lung Cancer Using Lidc Datasetmentioning

confidence: 99%

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Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

Gayap,

Akhloufi

2024

BioMedInformatics

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Deep learning has emerged as a powerful tool for medical image analysis and diagnosis, demonstrating high performance on tasks such as cancer detection. This literature review synthesizes current research on deep learning techniques applied to lung cancer screening and diagnosis. This review summarizes the state-of-the-art in deep learning for lung cancer detection, highlighting key advances, limitations, and future directions. We prioritized studies utilizing major public datasets, such as LIDC, LUNA16, and JSRT, to provide a comprehensive overview of the field. We focus on deep learning architectures, including 2D and 3D convolutional neural networks (CNNs), dual-path networks, Natural Language Processing (NLP) and vision transformers (ViT). Across studies, deep learning models consistently outperformed traditional machine learning techniques in terms of accuracy, sensitivity, and specificity for lung cancer detection in CT scans. This is attributed to the ability of deep learning models to automatically learn discriminative features from medical images and model complex spatial relationships. However, several challenges remain to be addressed before deep learning models can be widely deployed in clinical practice. These include model dependence on training data, generalization across datasets, integration of clinical metadata, and model interpretability. Overall, deep learning demonstrates great potential for lung cancer detection and precision medicine. However, more research is required to rigorously validate models and address risks. This review provides key insights for both computer scientists and clinicians, summarizing progress and future directions for deep learning in medical image analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Deep Learning Techniques For Lung Cancer Using Lidc Datasetmentioning

confidence: 99%

Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

Gayap,

Akhloufi

2024

BioMedInformatics

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“…The author used a CNN for classification purposes. In [17], optimized deformable methods and DL approaches were implemented for classifying and detecting lung cancer. The Bayesian fuzzy clustering has been enforced to segment the lung lobes and the median filter was concerned for pre-processing.…”

Section: Literature Reviewmentioning

confidence: 99%

“…( 5) 0𝑏𝑗 = min (𝐸𝑟𝑟𝑜𝑟) (15) The agents are ranked based on their fitness utilizing Eqs. ( 16) & (17). Now, 0𝑏𝑗 𝑟 is regarded as the sorted objective function based vector, and 𝐵 𝑟 represent the sorted population matrix: (17) Where 𝑀 and 𝐶 are utilized for selecting the population of mice and cats.…”

Section: Stage Iv: Parameter Tuning Using Scmo Algorithmmentioning

confidence: 99%

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Self-Upgraded Cat Mouse Optimizer With Machine Learning Driven Lung Cancer Classification on Computed Tomography Imaging

Ragab,

Katib,

Sharaf

et al. 2023

IEEE Access

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Machine learning (ML) roles a vital play in analysing lung cancer. Lung cancer has notoriously problem to analyse but it has progressed to late phase, accomplishing the main reason for cancer-related mortality. Lung cancer can be fatal if not early treatment, and accomplishing this is a crucial problem. A primary analysis of malignant nodules is frequently developed utilizing computed tomography (CT) and chest radiography (X-ray) scans; however, the risk of benign nodules causes wrong option. During these primary steps, malignant and benign nodules seem very same. Moreover, radiologists are a hard time categorizing and observing lung abnormalities. Lung cancer screenings carried out by radiologists are frequently applied with utilize of computer-aided diagnostic (CAD) technology. This study presents a new Self-Upgraded Cat Mouse Optimizer with Machine Learning Driven Lung Cancer Classification (SCMO-MLL2C) technique on CT images. The projected SCMO-MLL2C system mainly focuses on the identification and classification of CT images into three classes namely benign, malignant, and normal. To eradicate the noise in the CT images, the SCMO-MLL2C technique uses Gaussian filtering (GF) approach. Besides, densely connected networks (DenseNet-201) model for feature extraction process with slime mold algorithm (SMA) as a hyperparameter optimizer. In the presented SCMO-MLL2C technique, Elman Neural Network (ENN) approach was used for lung cancer classification. Furthermore, the SCMO approach has been employed for better parameter tuning of the ENN technique. To exhibit the performance validation of the SCMO-MLL2C system, the LIDC-IDRI database was utilized in this study. The simulation outcomes ensured the supremacy of the SCMO-MLL2C system over other existing approaches with maximum accuracy of 99.30%. INDEX TERMSComputer aided diagnosis; Cancer imaging; Lung cancer; Machine learning; CT images

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Identification of lung cancer using archimedes flow regime optimization enabled deep belief network

Gampala,

Ramya,

Maram

et al. 2024

Multimed Tools Appl

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Optimized Deformable Model-based Segmentation and Deep Learning for Lung Cancer Classification

Cited by 9 publications

References 22 publications

Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

Deep Machine Learning for Medical Diagnosis, Application to Lung Cancer Detection: A Review

Self-Upgraded Cat Mouse Optimizer With Machine Learning Driven Lung Cancer Classification on Computed Tomography Imaging

Identification of lung cancer using archimedes flow regime optimization enabled deep belief network

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