Detection and classification of lung cancer computed tomography images using a novel improved deep belief network with Gabor filters

Siddiqui, Ebtasam Ahmad; Chaurasia, Vijayshri; Shandilya, Madhu

doi:10.1016/j.chemolab.2023.104763

Cited by 41 publications

(10 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…E.A. Siddiqui et al [27] conducted an experiment using Gabor filters with an enhanced Deep Belief Network (E-DBN) to improve the performance of computer-aided lung cancer classifiers. The method uses Support Vector Machines (SVM) with E-DBN to classify the lung cancer nodules.…”

Section: Literature Reviewmentioning

confidence: 99%

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Faruqui,

Yousuf,

Kateb

et al. 2023

Heliyon

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Faruqui,

Yousuf,

Kateb

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…In [9], the authors presented a new model for lung CT image classification. The authors have used Gabor filters with an enhanced Deep Belief Network (E-DBN) with multiple classification methods.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Ragab,

Katib,

Sharaf

et al. 2023

IEEE Access

View full text Add to dashboard Cite

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

show abstract

“…These studies suggest that the automatic detection of different diseases is possible with the help of real-time CAD systems. Similarly, several studies have been conducted for detecting lung cancer using computer algorithms [4,[14][15][16]. In this regard, a CAD system that uses CT scan data to identify tumors in their early stages is proposed in [14].…”

Section: Introductionmentioning

confidence: 99%

“…High values were also found in the other performance parameters, such as 95% for specificity and 95.714% for sensitivity. In a different study, Gabor filters with an improved version of the deep belief network (DBN) were used with various classification methods to sort lung cancer [15]. Two cascaded restricted Boltzmann machines (RBMs), i.e., Gaussian-Bernoulli and Bernoulli-Bernoulli RBMs were used in the improved DBN.…”

Section: Introductionmentioning

confidence: 99%

A multichannel analysis of imbalanced computed tomography data for lung cancer classification

Sohaib,

Hasan,

Zheng

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Lung cancer holds the highest fatality rate among cancers, emphasizing the importance of early detection. Computer algorithms have gained prominence across various domains, including lung cancer diagnosis. These algorithms assist specialists, especially in medical imaging, yet current efforts lack comprehensive CT data analysis; especially in handling imbalanced datasets and fully exploiting spatial information. The lack of spatial analysis hinders the ability to identify subtle variations in texture and structure that are crucial for detecting lung cancer early and accurately. Therefore, this study uses a multichannel analysis of computed tomography (CT) images and deep learning-based ensemble learning (MC-ECNN) to find lung cancer even when the data is not balanced. Firstly, the data imbalance issue is tackled through the synthetic minority oversampling technique (SOMTE); afterwards, a multi-channel analysis of the data is performed to explore a distinct set of abstract features. Lastly, a deep ensemble learning method is used to classify the extracted distinct abstract feature set into the appropriate classes. The proposed method uses the discrete Fast Fourier transform (DFFT) and discrete cosine transform (DCT), along with the actual CT scans, for the multi-channel analysis of the data in different domains. The proposed model yielded 99.60% test accuracy on unseen data, which is at least 3% better than the other state-of-the-art studies considered for the comparison. In addition to the classification accuracy, the efficacy of the proposed model has also been justified through precision, recall, F1-score, support value, and misclassification rate.

show abstract

Detection and classification of lung cancer computed tomography images using a novel improved deep belief network with Gabor filters

Cited by 41 publications

References 33 publications

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

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

A multichannel analysis of imbalanced computed tomography data for lung cancer classification

Contact Info

Product

Resources

About