Lung Cancer Detection and Classification Based on Alexnet CNN

Agarwal, Aman; Patni, Kritik; Rajeswari, D

doi:10.1109/icces51350.2021.9489033

Cited by 43 publications

(19 citation statements)

References 14 publications

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“…Agarwal et al [25] investigated a framework to detect and classify lung cancer based on AlexNet CNN. In the first step, the green channel extracted was from the original color CT image.…”

Section: Related Workmentioning

confidence: 99%

“…The limitations of previous studies are mentioned, such as deep knowledge [17][18][19][20], which is required to obtain handcrafted features. The studies [25,29,31] were based on lesser amounts of images and on imbalanced datasets. The research works [22,23,26] focused on hybrid techniques that created complexity of the model, while different architectures were used in some research works [35][36][37] to improve accuracy.…”

Section: Related Workmentioning

confidence: 99%

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Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

Naseer

Akram

Masood

et al. 2022

Sensors

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The convolutional neural network (CNN) has become a powerful tool in machine learning (ML) that is used to solve complex problems such as image recognition, natural language processing, and video analysis. Notably, the idea of exploring convolutional neural network architecture has gained substantial attention as well as popularity. This study focuses on the intrinsic various CNN architectures: LeNet, AlexNet, VGG16, ResNet-50, and Inception-V1, which have been scrutinized and compared with each other for the detection of lung cancer using publicly available LUNA16 datasets. Furthermore, multiple performance optimizers: root mean square propagation (RMSProp), adaptive moment estimation (Adam), and stochastic gradient descent (SGD), were applied for this comparative study. The performances of the three CNN architectures were measured for accuracy, specificity, sensitivity, positive predictive value, false omission rate, negative predictive value, and F1 score. The experimental results showed that the CNN AlexNet architecture with the SGD optimizer achieved the highest validation accuracy for CT lung cancer with an accuracy of 97.42%, misclassification rate of 2.58%, 97.58% sensitivity, 97.25% specificity, 97.58% positive predictive value, 97.25% negative predictive value, false omission rate of 2.75%, and F1 score of 97.58%. AlexNet with the SGD optimizer was the best and outperformed compared to the other state-of-the-art CNN architectures.

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“…Agarwal et al [25] investigated a framework to detect and classify lung cancer based on AlexNet CNN. In the first step, the green channel extracted was from the original color CT image.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

Naseer

Akram

Masood

et al. 2022

Sensors

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show abstract

“…There has been a significant increase in the death rate from lung cancer in the last few years [1,4,5]. Healthcare providers and physicians count on different methods to diagnose lung cancers, including X-rays of the chest, CT scans, MRIs, and biopsies [6][7][8][9][10][11][12][13]. Initially, physicians determine the stage of cancer and its exact locations to prepare recovery treatment plans and tell patients what to expect in their path against the disease [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…Initially, physicians determine the stage of cancer and its exact locations to prepare recovery treatment plans and tell patients what to expect in their path against the disease [14][15][16]. In general, two types of lung cancer exist [1,8,9,17]:…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Decision Support System for Lung Cancer Diagnosis

Alsheikhy¹,

Said²,

Shawly³

2023

Computer Systems Science and Engineering

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Lung cancer is the leading cause of cancer-related death around the globe. The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis. Most diagnostic techniques can identify and classify only one type of lung cancer. It is crucial to close this gap with a system that detects all lung cancer types. This paper proposes an intelligent decision support system for this purpose. This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives. Its algorithm uses a Convolutional Neural Network (CNN) tool to perform deep learning and a Random Forest Algorithm (RFA) to help classify the type of cancer present using several extracted features, including histograms and energy. Numerous simulation experiments were conducted on MATLAB, evidencing that this system achieves 98.7% accuracy and over 98% precision and recall. A comparative assessment assessing accuracy, recall, precision, specificity, and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered metrics. This study found that using CNNs and RFAs is highly effective in detecting lung cancer, given the high accuracy, precision, and recall results. These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.

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“…The model in this paper achieves the accuracy of 0.78. Agarwal et al [21], the author employed a pre-trained  ISSN: 2502-4752 Indonesian J Elec Eng & Comp Sci, Vol. 29, No.…”

Section: Introductionmentioning

confidence: 99%

Osteoporosis detection using convolutional neural network based on dual-energy X-ray absorptiometry images

Mohammed

George

2022

IJEECS

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Osteoporosis is one of the most common diseases that affect the bones of adults, especially women in menopause, and the reason for this is due to the lack of bone mineral density bone mineral density (BMD). BMD can be measured by X-ray and dual energy X-ray absorptiometry (DEXA) images, this article, focused on using DEXA images for Osteoporosis detection. At first, the original image must passed through the preprocessing stage, during which the noisy parts is reduced, and the useless parts are eliminated, and then the contrast between adjacent areas is increased and the area of interest is allocated. After that, the image is passed in a deep learning model in order to extract the unique features on the basis of which each image is classified. The classification result was excellent with 98% accuracy. The used dataset is “Osteoporosis DEXA scans images” of Spine from Pakistan.

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Lung Cancer Detection and Classification Based on Alexnet CNN

Cited by 43 publications

References 14 publications

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

Performance Analysis of State-of-the-Art CNN Architectures for LUNA16

An Intelligent Decision Support System for Lung Cancer Diagnosis

Osteoporosis detection using convolutional neural network based on dual-energy X-ray absorptiometry images

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