Classification of malignant lung cancer using deep learning

Kumar, Vinod; Bakariya, Brijesh

doi:10.1080/03091902.2020.1853837

Cited by 13 publications

(3 citation statements)

References 14 publications

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“…Subramanian et al [48] proposed a deep learning framework using CNN based methods such as AlexNet, LeNet and VGG16. Kumar and Bakariya [51] used deep learning methods to find the presence of malignant cancers in CT images. Kriegsmann et al [52] used deep learning models to classify and differentiate non-small cell lung cancer from small cell lung cancer.…”

Section: B Deep Learning Methodsmentioning

confidence: 99%

Systematic Review of Deep Learning Techniques for Lung Cancer Detection

Aharonu¹,

Kumar²

2023

IJACSA

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Cancer is the leading cause of deaths across the globe and 10 million people died of cancer and particularly 2.21 million new cases registered besides 1.80 million deaths, according to WHO, in 2020. Malignant cancer is caused by multiplication and growth of lung cells. In this context, exploiting technological innovations for automatic detection of lung cancer early is to be given paramount importance. Towards this end significant progress has been made and deep learning model such as Convolutional Neural Network (CNN) is found superior in processing lung CT or MRI images for disease diagnosis. Lung cancer detection in the early stages of the disease helps in better treatment and cure of the disease. In this paper, we made a systematic review of deep learning methods for detection of lung cancer. It reviews peer reviewed journal papers and conferences from 2012 to 2021. Literature review throws light on synthesis of different existing methods covering machine learning (ML), deep learning and artificial intelligence (AI). It provides insights of different deep learning methods in terms of their pros and cons and arrives at possible research gaps. This paper gives knowledge to the reader on different aspects of lung cancer detection which can trigger further research possibilities to realize models that can be used in Clinical Decision Support Systems (CDSSs) required by healthcare units.

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Section: B Deep Learning Methodsmentioning

confidence: 99%

Systematic Review of Deep Learning Techniques for Lung Cancer Detection

Aharonu¹,

Kumar²

2023

IJACSA

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“…The method achieved results that exceeded the techniques of machine learning. Vinod et al [ 22 ] designed a method to identify pulmonary nodules from regions of interest. The watershed algorithm and Gabor filter were applied to segment the lung regions.…”

Section: Related Workmentioning

confidence: 99%

Histopathological Analysis for Detecting Lung and Colon Cancer Malignancies Using Hybrid Systems with Fused Features

et al. 2023

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Lung and colon cancer are among humanity’s most common and deadly cancers. In 2020, there were 4.19 million people diagnosed with lung and colon cancer, and more than 2.7 million died worldwide. Some people develop lung and colon cancer simultaneously due to smoking which causes lung cancer, leading to an abnormal diet, which also causes colon cancer. There are many techniques for diagnosing lung and colon cancer, most notably the biopsy technique and its analysis in laboratories. Due to the scarcity of health centers and medical staff, especially in developing countries. Moreover, manual diagnosis takes a long time and is subject to differing opinions of doctors. Thus, artificial intelligence techniques solve these challenges. In this study, three strategies were developed, each with two systems for early diagnosis of histological images of the LC25000 dataset. Histological images have been improved, and the contrast of affected areas has been increased. The GoogLeNet and VGG-19 models of all systems produced high dimensional features, so redundant and unnecessary features were removed to reduce high dimensionality and retain essential features by the PCA method. The first strategy for diagnosing the histological images of the LC25000 dataset by ANN uses crucial features of GoogLeNet and VGG-19 models separately. The second strategy uses ANN with the combined features of GoogLeNet and VGG-19. One system reduced dimensions and combined, while the other combined high features and then reduced high dimensions. The third strategy uses ANN with fusion features of CNN models (GoogLeNet and VGG-19) and handcrafted features. With the fusion features of VGG-19 and handcrafted features, the ANN reached a sensitivity of 99.85%, a precision of 100%, an accuracy of 99.64%, a specificity of 100%, and an AUC of 99.86%.

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“…Characteristics commonly found in CADx systems for diagnosis of nodule malignancy include but are not limited to 2D Convolutional Neural Networks (2D CNNs) [12] [13], multi-view 2D CNNs [14], 3D CNNs [15] [16], and multi-view 3D Squeeze and Excitation CNNs [20]. Some current research applies secondary methods such as Gabor filters [17] [18][19] and attention modules [21] in conjunction with the aforementioned network architectures to further improve classification results. For both network training as well as the evaluation and testing, the majority of approaches employ the Lung Image Database Consortium Image Collection (LIDC) database [22].…”

Section: Introductionmentioning

confidence: 99%

Performance of a bank of 2D efficient-nets for benign/malignant classification of lung nodules in 3D thoracic x-ray CT scans

Settle

Veasey

Seow

et al. 2023

Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging

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Lung cancer is the leading cause of cancer-related death among both men and women and second most commonly diagnosed cancer, accounting for 18% of the total cancer deaths world-wide [4]. Screening high-risk patients with low dose Computed Tomography (CT) can lead to earlier treatment and increase the survival rate [5]. However, cancer diagnosis remains a challenging problem due to the subtle visual differences between benign and malignant nodules in CT images. Hence, computer-aided diagnosis (CADx) systems may prove useful in assisting radiologists in the malignancy prediction task. Previously we developed a convolutional attention-based network that allows for use of pre-trained 2-D convolutional feature extractors and is extendable to multi-timepoint classification in a Siamese structure [6]. The use of a sequence of parallel 2-D CNNs in place of a 3D CNN will result in significant reduction in the number of network parameters. In this paper, we keep the same overall structure utilized in [6] including the attention mechanism. However, herein, we report on use of Efficient-Net [1] for 2-D feature extractors due to its success on the Image-Net classification challenge. Variations of the Efficient-Net B0 to B7 pretrained on Image-Net were fine-tuned and applied to NLSTx data [6] a subset of data acquired in the National Lung Screening Trial (NLST) [2]. NLSTx includes data from biopsy confirmed scans in 650 benign and 207 malignant nodules at up to 3 time points. In our study, the performance of the best Efficient-Net reached an area under ROC curve of .7896 for the benign/malignant classification.

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Classification of malignant lung cancer using deep learning

Cited by 13 publications

References 14 publications

Systematic Review of Deep Learning Techniques for Lung Cancer Detection

Systematic Review of Deep Learning Techniques for Lung Cancer Detection

Histopathological Analysis for Detecting Lung and Colon Cancer Malignancies Using Hybrid Systems with Fused Features

Performance of a bank of 2D efficient-nets for benign/malignant classification of lung nodules in 3D thoracic x-ray CT scans

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