Detection and classification of lung cancer using CNN and Google net

Pandian, R.; Vedanarayanan, V.; Kumar, D.N.S. Ravi; RajaKuma, R.

doi:10.1016/j.measen.2022.100588

Cited by 26 publications

(9 citation statements)

References 6 publications

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“…Q = {q1, q2, q3, …., qm} (13) Step 4: Decomposition of an optimal alternative into two subcomponents Q max and Q min is performed. The maximum number of beneficial criteria is denoted by k to produce Q max ; meanwhile, the maximum number of nonbeneficial criteria is denoted by h = m-k to yield Q min .…”

Section: Multi-criteria Decision-making Approachmentioning

confidence: 99%

“…In fact, the CNN technique has extensive applications in disease diagnosis. The performance of CNNs in a variety of cancer detection and classification tasks, such as breast cancer [10], prostate cancer [11], liver lesions [12], and lung cancer [13], is particularly encouraging. According to Sudharshan et al [14], employing CNN models enhances the performance of diagnostic systems.…”

Section: Introductionmentioning

confidence: 99%

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Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

Saleh,

Hassan,

Salaheldin

2024

Preprint

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Skin cancer can be treated if it is detected early. Many artificial intelligence-based models have been developed for skin cancer detection and classification. Considering the development of multiple models according to various scenarios and selecting the optimum model, these models were rarely considered in previous works. This study aimed to develop multiple models for skin cancer classification and select the optimum model. Convolutional neural networks (CNNs) in the form of AlexNet, Inception V3, MobileNet V2, and ResNet 50 were used for feature extraction. Feature reduction was carried out using two algorithms of the gray wolf optimizer (GWO) in addition to using the original features. Skin cancer images were classified into four classes based on six machine learning (ML) classifiers. As a result, 51 models were developed with different combinations of CNN algorithms, without GWO algorithms, with two GWO algorithms, and with six ML classifiers. To select the optimum model with the best results, the multicriteria decision-making approach was utilized in the recent form of ranking the alternatives by perimeter similarity (RAPS). Model training and testing were conducted using the International Skin Imaging Collaboration (ISIC) 2017 dataset. Based on nine evaluation metrics and according to the RAPS method, the AlexNet algorithm with GWO yielded the optimum model, achieving a classification accuracy of 94.5%. This work presents the first study on benchmarking skin cancer classification with a large number of models. Feature reduction not only reduces the time spent on training but also improves classification accuracy. The RAPS method has proven its robustness in the problem of selecting the best model for skin cancer classification.

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Section: Multi-criteria Decision-making Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

Saleh,

Hassan,

Salaheldin

2024

Preprint

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“…Convolutional neural network methodology has been used to process the JPEGencoded DICOM images of the lungs and it helps in identifying the abnormalities, if present [4]. Image processing methods like feature extraction, histogram equalization, grayscale conversion and thresholding are useful in identifying the abnormalities in the images [5]. To boost up the speed and accuracy of the lung cancer detection machine learning techniques are used [6].…”

Section: Introductionmentioning

confidence: 99%

An Effective Lung Cancer Diagnosis Model Using the CNN Algorithm

Kukreja,

Sabharwal,

D S

2024

Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security,

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Lung cancer is a very complicated disease and can be deadly if not detected on time or at the early phase. A cost effective and more accurate methodology is required to diagnose the lung cancer at an early stage. It results in improved diagnosis, saving the money as well as time and in simplifying the operations. This study focused on the 3Dimensional CNN architecture by categorizing the imaging data or histopathological images in three kinds of cancer: squamous cell carcinoma, adenocarcinoma, and Benign. The current research categorize these 3 types of cancer by implementing the CNN architecture and achieving the better accuracy by comparing with the other methologies used for the diagnosis of lung cancer. While implementing CNN method, accuracy achieved in training stage was 96.11 and accuracy achieved in validation was 97.2%. This proposed method has the potential to improve the detection of lung cancer by grouping them according to the symptoms they have. In this research, along with CNN, random forest technique has been used to to reduce the resources, labour required and time also. CNN model achieved the improved detection accuracy for lung cancer and saving the lives by indulging early disease recognition.

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“…In healthcare imaging, machine learning has been utilized for disease diagnosis in breast [ 9 , 10 ], brain [ 11 , 12 , 13 ], and lung [ 14 , 15 ] tumors. Recently, research on brain tumor segmentation, with numerous segmentation methods using different datasets, has increased considerably.…”

Section: Introductionmentioning

confidence: 99%

WBM-DLNets: Wrapper-Based Metaheuristic Deep Learning Networks Feature Optimization for Enhancing Brain Tumor Detection

et al. 2023

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This study presents wrapper-based metaheuristic deep learning networks (WBM-DLNets) feature optimization algorithms for brain tumor diagnosis using magnetic resonance imaging. Herein, 16 pretrained deep learning networks are used to compute the features. Eight metaheuristic optimization algorithms, namely, the marine predator algorithm, atom search optimization algorithm (ASOA), Harris hawks optimization algorithm, butterfly optimization algorithm, whale optimization algorithm, grey wolf optimization algorithm (GWOA), bat algorithm, and firefly algorithm, are used to evaluate the classification performance using a support vector machine (SVM)-based cost function. A deep-learning network selection approach is applied to determine the best deep-learning network. Finally, all deep features of the best deep learning networks are concatenated to train the SVM model. The proposed WBM-DLNets approach is validated based on an available online dataset. The results reveal that the classification accuracy is significantly improved by utilizing the features selected using WBM-DLNets relative to those obtained using the full set of deep features. DenseNet-201-GWOA and EfficientNet-b0-ASOA yield the best results, with a classification accuracy of 95.7%. Additionally, the results of the WBM-DLNets approach are compared with those reported in the literature.

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Detection and classification of lung cancer using CNN and Google net

Cited by 26 publications

References 6 publications

Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making

An Effective Lung Cancer Diagnosis Model Using the CNN Algorithm

WBM-DLNets: Wrapper-Based Metaheuristic Deep Learning Networks Feature Optimization for Enhancing Brain Tumor Detection

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