Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification

Ragab, Mahmoud; Abdushkour, Hesham A.; Nahhas, Alaa F.; Aljedaibi, Wajdi

doi:10.32604/cmc.2022.028856

Cited by 12 publications

(9 citation statements)

References 12 publications

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“…The standard activation function in MobileNetV2 is replaced by the exponential linear unit activation function for the classification. A deep CNN architecture is discussed in [15] for lung cancer classification. Two preprocessing steps, noise removal and enhancement, are performed before classification.…”

Section: Related Workmentioning

confidence: 99%

Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis

Ramapraba¹,

Epsiba²,

Umapathy³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns. In this work, an Auxiliary Classifier (AC)-Generative Adversarial Network (GAN) based Lung Cancer Classification (LCC) system is developed. The proposed AC-GAN-LCC system consists of three modules; preprocessing, Lungs Region Detection (LRD), and AC-GAN classification. A Wiener filter is employed in the preprocessing module to remove the Gaussian noise. In the LRD module, only the lung regions (left and right lungs) are detected using iterative thresholding and morphological operations. In order to extract the lung region only, flood filling and background subtraction. The detected lung regions are fed to the AC-GAN classifier to detect the nodules. It classifies the nodules into one of the two classes, i.e., binary classification (such as nodules or non-nodules). The AC-GAN is the extended version of the conditional GAN that predicts the label of a given image. Three different optimization techniques, adaptive gradient optimization, root mean square propagation optimization, and Adam optimization are employed for optimizing the AC-GAN architecture. The proposed AC-GAN-LCC system is evaluated on the Lung Image Database Consortium (LIDC) database Computed Tomography (CT) scan images. The proposed AC-GAN-LCC system classifies ∼15000 CT slices (7310 non-nodules and 7685 nodules). It provides an overall accuracy of 98.8% on the LIDC database using Adam optimization by a 10-fold cross-validation approach.

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Section: Related Workmentioning

confidence: 99%

Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis

Ramapraba¹,

Epsiba²,

Umapathy³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…Here, the medical data was taken as the sharing data. To realize data sharing under the premise of protecting data privacy, we use horizontal federated learning technology, so each participant has the same data features [25][26][27][28].…”

Section: Implementation Of Data Sharing Schemementioning

confidence: 99%

Research on Federated Learning Data Sharing Scheme Based on燚ifferential燩rivacy

Guo¹

2023

Computers, Materials &Amp; Continua

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To realize data sharing, and to fully use the data value, breaking the data island between institutions to realize data collaboration has become a new sharing mode. This paper proposed a distributed data security sharing scheme based on C/S communication mode, and constructed a federated learning architecture that uses differential privacy technology to protect training parameters. Clients do not need to share local data, and they only need to upload the trained model parameters to achieve data sharing. In the process of training, a distributed parameter update mechanism is introduced. The server is mainly responsible for issuing training commands and parameters, and aggregating the local model parameters uploaded by the clients. The client mainly uses the stochastic gradient descent algorithm for gradient trimming, updates, and transmits the trained model parameters back to the server after differential processing. To test the performance of the scheme, in the application scenario where many medical institutions jointly train the disease detection system, the model is tested from multiple perspectives by taking medical data as an example. From the testing results, we can know that for this specific test dataset, when the parameters are properly configured, the lowest prediction accuracy rate is 90.261% and the highest accuracy rate is up to 94.352. It shows that the performance of the model is good. The results also show that this scheme realizes data sharing while protecting data privacy, completes accurate prediction of diseases, and has a good effect.

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“…Likewise, the precise grading of the colon and lung tumor subtypes necessitates a trained pathologist, and manual grading can be prone to error. Now, automatic image processing techniques are being applied for lung tumors [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection

AlGhamdi

Asar

Assiri

et al. 2023

Cancers

Self Cite

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An early diagnosis of lung and colon cancer (LCC) is critical for improved patient outcomes and effective treatment. Histopathological image (HSI) analysis has emerged as a robust tool for cancer diagnosis. HSI analysis for a LCC diagnosis includes the analysis and examination of tissue samples attained from the LCC to recognize lesions or cancerous cells. It has a significant role in the staging and diagnosis of this tumor, which aids in the prognosis and treatment planning, but a manual analysis of the image is subject to human error and is also time-consuming. Therefore, a computer-aided approach is needed for the detection of LCC using HSI. Transfer learning (TL) leverages pretrained deep learning (DL) algorithms that have been trained on a larger dataset for extracting related features from the HIS, which are then used for training a classifier for a tumor diagnosis. This manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique. The purpose of the study is to detect LCC effectually in histopathological images. To execute this, the BERTL-HIALCCD method follows the concepts of computer vision (CV) and transfer learning for accurate LCC detection. When using the BERTL-HIALCCD technique, an improved ShuffleNet model is applied for the feature extraction process, and its hyperparameters are chosen by the BER system. For the effectual recognition of LCC, a deep convolutional recurrent neural network (DCRNN) model is applied. Finally, the coati optimization algorithm (COA) is exploited for the parameter choice of the DCRNN approach. For examining the efficacy of the BERTL-HIALCCD technique, a comprehensive group of experiments was conducted on a large dataset of histopathological images. The experimental outcomes demonstrate that the combination of AER and COA algorithms attain an improved performance in cancer detection over the compared models.

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Deer Hunting Optimization with Deep Learning Model for Lung Cancer Classification

Cited by 12 publications

References 12 publications

Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis

Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis

Research on Federated Learning Data Sharing Scheme Based on燚ifferential燩rivacy

Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection

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