Lung Tumor Classification and Detection from CT Scan Images using Deep Convolutional Neural Networks (DCNN)

Mohanapriya, N.; Kalaavathi, B.; Kuamr, T. senthil

doi:10.1109/iccike47802.2019.9004247

Cited by 19 publications

(7 citation statements)

References 4 publications

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“…The discriminative network adopts a classification model. This study first uses the texture features of glands to identify the presence of individual glandular structures; then, the texture features and morphometric obtained from glandular units are applied to the classification stage, and finally the images are labeled as grades 1 to 5 [ 5 ]. The literature shows that the texture features of the image are represented according to the different power spectra of the image [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network

Tyagi

Tyagi²,

Choudhury³

et al. 2022

BioMed Research International

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Prostate cancer is one of the most common cancers in men worldwide, second only to lung cancer. The most common method used in diagnosing prostate cancer is the microscopic observation of stained biopsies by a pathologist and the Gleason score of the tissue microarray images. However, scoring prostate cancer tissue microarrays by pathologists using Gleason mode under many tissue microarray images is time-consuming, susceptible to subjective factors between different observers, and has low reproducibility. We have used the two most common technologies, deep learning, and computer vision, in this research, as the development of deep learning and computer vision has made pathology computer-aided diagnosis systems more objective and repeatable. Furthermore, the U-Net network, which is used in our study, is the most extensively used network in medical image segmentation. Unlike the classifiers used in previous studies, a region segmentation model based on an improved U-Net network is proposed in our research, which fuses deep and shallow layers through densely connected blocks. At the same time, the features of each scale are supervised. As an outcome of the research, the network parameters can be reduced, the computational efficiency can be improved, and the method’s effectiveness is verified on a fully annotated dataset.

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Section: Introductionmentioning

confidence: 99%

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network

Tyagi

Tyagi²,

Choudhury³

et al. 2022

BioMed Research International

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“…The methodologies used in this study to identify lung problems include merging the analysis of consumer data with data from chest X-rays, as well as employing the Caps Net network and CNN's well-known pre-trained model for this type of data. N. Mohanapriya [11] using Deep Convolutional Neural Networks (DCNN),Lung Tumor Classification and Detection from CT Scan Images in 2019 [12]. Maximum or average pooling replaces input values with maximum or average values to lessen output sensitivity to minute input changes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The images of CT [10] Pneumonia are supplied into the system and converted to pixels. Holes in the Pneumonia lobes are examined using the active contour approach.…”

Section: Proposed Systemmentioning

confidence: 99%

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Xception Framework for Predicting Pneumonitis Using Deep Learning

et al. 2023

Recent Trends in Data Science and Its Applications

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Pneumonia is a lung disorder that can be brought on by infections with bacteria, viruses, and fungi. To identify the location of inflammation in a picture, a pneumonia detection system is being built. An experienced radiologist can identify the illness from chest X-ray images. This will examine how various pre-processing methods, including X-rays, can identify and categories numerous diseases. Computer-aided diagnosis systems are therefore required to direct the practitioners. For the purpose of predicting pneumonia, this system employs a deep neural network algorithm using the Xception framework. CT lung pictures are entered into the system and converted to pixels.Active contour technique analysis is used to determine the presence of holes in the lung lobes. The classification of CT scan images for consumer with coronavirus infections using CNN models is the main goal of this study. The test results revealed that the software can distinguish between photos that are infected and those that are not. The Xception network detects pneumonia cases more effectively.

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“…Image processing methods like threshold segmentation [7], clustering [8], and morphological and regional analysis [9], [10] are widely adopted to identity tumor cells/tissues. Thanks to the development of machine learning, some more efficient methods, including SVM (Support Vector Machine) [11], BP neural network [12] and deep neural network [13][14][15][16], have been applied to tumor cell image recognition.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Tumor Cell Detection Method Based on Circulating Tumor Cell (CTC) Technology

Wu¹,

Wu²,

Huang³

et al. 2023

Journal of Compurters

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<p>Cancer has become one of the greatest threats for human life. Doctors can get original images of the sick organs with the assistance of medical technology. However, manual interpretation of the original images is time consuming and labor consuming. Nowadays, the intelligent detection of tumor cell images is commonly adopted in cancer diagnosis. In this paper, we propose Imporved Selective Search (ISS) algorithm and CTCNet based on Circulating Tumor Cell (CTC) technology to improve the cancer images’ detection efficiency. CTCs can be collected by a sampling needle with EpCAM antibody which can specifically bind to tumor cells. After fluorescent staining process, images obtained from sampling needle will be processed by the ISS algorithm for candidate region preselection. All of the eligible areas are evaluated with a self-designed neural network called CTCNet, resulting in efficient recognition of circulating tumor cells. During this process, all algorithms are accelerated through the GPU and NPU hardware platform, which further improves the detection speed of the system. In the experiment, we first verify the efficiency of proposed ISS algorithm by compared with Original Selective Search (OSS), we found that the number of candidate boxes reduced from 549 to 16 and the time consuming reduced by 0.3s after adopting the ISS algorithm. In order to evaluate the performance of the proposed 7-layer CTCNet, we compared CTCNet with SVM, BP neural network, AlexNet and VGGNet by using the dataset of 12312 samples from 30 patients, among them, there were 12 patients with early cancer and 18 patients with advanced cancer. And we got the highest recognition accuracy of 97.95% on CTCNet, which even beyond the VGGNet with deeper layers. In contrast to other combinations, we detect CTC in diverse clinical CTC images, the joint application of ISS algorithm and CTCNet achieves an outstanding system performance with accuracy up to 94.03% in the whole view images. Meanwhile, because of the application of a lightweight network in different hardware acceleration platform, the detection time of the CTC image in a single view can be less than 12s.</p> <p> </p>

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Lung Tumor Classification and Detection from CT Scan Images using Deep Convolutional Neural Networks (DCNN)

Cited by 19 publications

References 4 publications

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network

Xception Framework for Predicting Pneumonitis Using Deep Learning

Intelligent Tumor Cell Detection Method Based on Circulating Tumor Cell (CTC) Technology

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