An intelligent algorithm for lung cancer diagnosis using extracted features from Computerized Tomography images

Maleki, Negar; Niaki, Seyed Taghi Akhavan

doi:10.1016/j.health.2023.100150

Cited by 18 publications

(4 citation statements)

References 32 publications

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“…This approach automates lung cancer anatomical localization from PET/CT scans and classifies tumors with an average accuracy of 95.5%. N. Maleki et al [24] combined CNN with Artificial Neural Network (ANN) for initial lung cancer classification from CT images. Later, they applied Gradient Boosting (GB), Random Forest (RF), and Support Vector Machine (SVM) on numerical features of the same image dataset as the second approach.…”

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

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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

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“…The proposed DFD-Net discovered to better lung cancer detection results in improved accuracy, sensitivity and specificity. In [46], Computerized Tomography (CT) images of lung cancer patients for the early diagnosis of the disease. The raw CT images were CNN followed by ANN without any preprocessing.…”

Section: Literature Reviewmentioning

confidence: 99%

Residual Partial Derivative Multilayer Perceptron Deep Learning Based Classifier for Lung Cancer Detection

Padmanandam,

Khilar,

Pitla

et al. 2023

Preprint

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Lung cancer is a significant and frequent types of cancer globally both in patients and mortality rate. Computed Tomography (CT) test is efficient for early lung cancer detection due to potentiality in constructing lung images, generating nodules both in high resolution and tumor detection. But, early detection of lung cancer is obtained as difficult and time-consuming task. Also, detection of pulmonary nodules is a main method for early detection of lung cancer that can even heavily improve survival rate of lung cancer patients by reduced mortality rate to better level. Moreover, correct precision and incorrect detection of positive cancerous cell for lung nodules is both low and high. A lung cancer detection method using deep learning called, Residual Neural and Partial Derivative Multilayer Perceptron (RN-PDMP) is proposed. Initially the aid of lung cancer images acquired from IQ-OTH/NCCD Lung Cancer Dataset addresses class imbalance issue, therefore reducing the noise to a greater extent using Cross Entropy Focal Loss-based preprocessing model. Secondly preprocessed images as input, robust features are extracted by means of Census Transformed Residual Neural Network-based feature extraction algorithm. Finally, classification between normal, benign and malignant are made using Partial Derivative Multilayer Perceptron Deep Learning-based Classifier. The performance analysis of results describe to lung cancer detection model based on RN-PDMP integrating Forward Propagation-based Residual Learning has achieved a precision of 99% and recall of 97%. The results are comparatively better than existing methods in RN-PDMP method of lung cancer detection based on Partial Derivative-based back propagation is better recall for small samples and improves true positive pulmonary nodules.

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“…Other than that, adding to many features into classifier would harm its performance. Some researchers had stated that finding meaningful patterns is more challenging when there Lung cancer classification [17] Local binary pattern (LBP) based features Nodule vs non-nodule [18] Gabor features Lung cancer diagnosis [19] Wavelet features, texture features and histogram features Lung cancer prediction [20] Super pixels features Tumour vs non-tumour vs fundus (segmentation) [21] Morphological features, genomic features, and molecular features Tumour vs non-tumour…”

Section: Introductionmentioning

confidence: 99%

Enhancing lung lesion localization in CT-scans: a novel approach using FE_CXY and statistical analysis

Jafery,

Sulaiman,

Osman

et al. 2024

IJEECS

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Intelligence algorithm systems rely on a large dataset to effectively extract significant features that can recognize patterns for classification purposes and extensively utilized to assist the physicians in diagnosis of lung cancer. Extracting valuable features from the available dataset is crucial, especially in cases where additional real data may not be readily accessible. In this context, we propose a novel method called feature extraction based on centroid (FE_CXY) for lesion localization, utilizing a statistical approach. The approach begins with a segmentation process that employs image processing techniques to extract features of interest which is data centroid. This extracted data is then used to compute statistical measurements, revealing hidden patterns that contribute to distinguishing between lesion and non-lesion locations. The method’s efficiency is reflected in the development of robust models with improved performance in localizing lung lesions. The study’s statistical findings strongly indicate that FE_CXY plays a crucial role as an important feature for detecting lesion localization supported by a student’s t-test, which identifies a statistically significant difference in the patterns between lesion and non-lesion localization (p<0.05). By incorporating this method into lung cancer detection systems, we anticipate improved accuracy and efficacy, thereby benefiting early diagnosis and treatment planning.

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An intelligent algorithm for lung cancer diagnosis using extracted features from Computerized Tomography images

Cited by 18 publications

References 32 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

Residual Partial Derivative Multilayer Perceptron Deep Learning Based Classifier for Lung Cancer Detection

Enhancing lung lesion localization in CT-scans: a novel approach using FE_CXY and statistical analysis

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