Early Lung Cancer Detection using Deep Learning Optimization

Elnakib, Ahmed; Amer, Hanan M.; Abou-Chadi, Fatma E. Z.

doi:10.3991/ijoe.v16i06.13657

Cited by 50 publications

(28 citation statements)

References 24 publications

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“…Other than that, there are also research on offline signature verification using deep learning CNN [17]. Some works used googlenet from CNN for lung cancer detection [18,19]. However, this paper is focused on image classification of 5 WBC types as CNN works wonderfully and able to provide high performance on image classification [20].…”

Section: Fig 1 5 Types Of Wbcmentioning

confidence: 99%

Cross Validation Analysis of Convolutional Neural Network Variants with Various White Blood Cells Datasets for the Classification Task

2022

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White Blood Cells (WBCs) analysis is an important procedure to detect diseases is that closely related to human immunity system. Manual WBCs analysis is laborious and hence computer aided system (CAD) is a better option to alleviate the shortcoming. Since conventional segmentation-classification approach is tedious to configure, a Convolutional Neural Network (CNN) become recent trend for WBCs classification. Previously, there are many works proposed for WBCs identification. However, the models that can be generalised to works well among various datasets is remain vague. In this paper, an analysis of various CNN models which are simple Alexnet, embedded friendly Mobilenet, inception based Googlenet, systematic architecture VGG-16 and skip connection based model (Resnet & Densenet), are tested with three major WBCs datasets (Kaggle, LISC and IDB-2). From the rigorous experiments, it can be concluded that simple CNN model of Alexnet performs well across all three datasets with 98.08% accuracy on Kaggle, 96.34% accuracy on IDB-2 and 84.52% on LISC. This outcome can be utilise as a basis to improve the CNN classification model that can be generalize to works under various WBCs datasets.

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Section: Fig 1 5 Types Of Wbcmentioning

confidence: 99%

Cross Validation Analysis of Convolutional Neural Network Variants with Various White Blood Cells Datasets for the Classification Task

2022

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“…Early identification of lung nodes from low dose computed tomography (LDCT) images was suggested by Elnakib et al [42]. Initially, the proposed device processes the raw data in order to increase the comparison between low-dose videos.…”

Section: Deep Learningmentioning

confidence: 99%

Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques

Abdullah

Abdulazeez

Sallow

2021

QAJ

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Lung cancer is one of the leading causes of mortality in every country, affecting both men and women. Lung cancer has a low prognosis, resulting in a high death rate. The computing sector is fully automating it, and the medical industry is also automating itself with the aid of image recognition and data analytics. This paper endeavors to inspect accuracy ratio of three classifiers which is Support Vector Machine (SVM), K-Nearest Neighbor (KNN)and, Convolutional Neural Network (CNN) that classify lung cancer in early stage so that many lives can be saving. Basically, the informational indexes utilized as a part of this examination are taken from UCI datasets for patients affected by lung cancer. The principle point of this paper is to the execution investigation of the classification algorithms accuracy by WEKA Tool. The experimental results show that SVM gives the best result with 95.56%, then CNN with CNN 92.11% and KNN with 88.40%.

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“…Fitness function used by SFLA for optimization is the minimization of the mean square error (MSE). The optimization problem thus, is based on minimizing the function stated in equation (5). The distribution of the sorted population to m number of memeplexes is done in such a way, that, the first population is assigned to first memeplex and second population is assigned to second memeplex and the same process is continued till the ℎ memeplex.…”

Section: Working Principle Of Proposed Sfla-elm Classifiermentioning

confidence: 99%

“…This is the reason why use of automated image analysis methods utilizing machine learning and image processing techniques are of wide use in recent years in the field of MR image processing. These computers assisted diagnosis (CAD) techniques not only reduce burden on the radiologist and neurologists but also improve the accuracy and objectivity of diagnosis [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Biologically Inspired ELM-based Framework for Classification of Brain MRIs

Pati

Mishra

Borah³

2020

Int. J. Onl. Eng.

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<span style="font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Use of medical images for clinical analysis of various critical diseases have become increasingly predominant in modern health care systems. Application of machine learning technique in this context evolves as a potential solution in terms providing faster output with high diagnostic accuracy. In this work we propose an Extreme Learning Machine (ELM) based classifier SFLA-ELM for detection of normal and pathological brain condition from brain Magnetic Resonance Images (MRIs). ELM is known for its speed and accuracy whereas the proposed method uses a swarm based evolutionary technique Shuffled Frog Leaping Algorithm (SFLA) and 10-fold cross validation method to optimally determine the network parameter of the ELM for better classification performance.</span><span style="font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Droid Sans Fallback'; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> The proposed model is experimented on three different brain MRI datasets of three different brain diseases. </span><span style="font-size: 11.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">To get better approximation accuracy and generalization ability for the base ELM classifier, the suitable activation function and the appropriate number of hidden layer nodes are chosen. The performance validation of the proposed framework is done under two different network conditions, i.e. fixed network structure and varying network structure, by comparing its performance with two standard hybridized ELM classifiers, namely, PSO-ELM and ABC-ELM. The comparative performance analysis suggests that the proposed SFLS-ELM gives better classification performance in diagnosing the diseases in terms of accuracy, sensitivity, specificity, F-score and Area under ROC curve (AUC).Furthermore, the SFLA-ELM also found to offer better generalization ability and better stability with more compact network structure.</span><span style="font-size: 22.0pt; mso-bidi-font-size: 16.0pt; line-height: 107%; font-family: 'Times New Roman',serif; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: EN-IN; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">A Biologically Inspired ELM-based Framework for Classification of Brain MRIs</span>

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Early Lung Cancer Detection using Deep Learning Optimization

Cited by 50 publications

References 24 publications

Cross Validation Analysis of Convolutional Neural Network Variants with Various White Blood Cells Datasets for the Classification Task

Cross Validation Analysis of Convolutional Neural Network Variants with Various White Blood Cells Datasets for the Classification Task

Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques

A Biologically Inspired ELM-based Framework for Classification of Brain MRIs

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