Analysis of Lung Tumour Detection and Segmentation Using Level Set Method of Active Contour Model

Gopi, K.; Selvakumar, J.

doi:10.14419/ijet.v7i4.10.21028

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Snake's model was utilized to develop a curve that aids in the detection of a tumor portion in the relevant photos. The curve must be drawn around the identified item, and then, it must alter its location towards the interior and end at the object borders [ 28 ]. For each contour line that gathers a similar amount of control point and distinct point, the initial prediction model of active contour is utilized; it predicts contour point that helps for the contour lines and then generates the active contour model's first prediction [ 29 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques

Kasinathan¹,

Jayakumar²

2022

BioMed Research International

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Artificial intelligence (AI), Internet of Things (IoT), and the cloud computing have recently become widely used in the healthcare sector, which aid in better decision-making for a radiologist. PET imaging or positron emission tomography is one of the most reliable approaches for a radiologist to diagnosing many cancers, including lung tumor. In this work, we proposed stage classification of lung tumor which is a more challenging task in computer-aided diagnosis. As a result, a modified computer-aided diagnosis is being considered as a way to reduce the heavy workloads and second opinion to radiologists. In this paper, we present a strategy for classifying and validating different stages of lung tumor progression, as well as a deep neural model and data collection using cloud system for categorizing phases of pulmonary illness. The proposed system presents a Cloud-based Lung Tumor Detector and Stage Classifier (Cloud-LTDSC) as a hybrid technique for PET/CT images. The proposed Cloud-LTDSC initially developed the active contour model as lung tumor segmentation, and multilayer convolutional neural network (M-CNN) for classifying different stages of lung cancer has been modelled and validated with standard benchmark images. The performance of the presented technique is evaluated using a benchmark image LIDC-IDRI dataset of 50 low doses and also utilized the lung CT DICOM images. Compared with existing techniques in the literature, our proposed method achieved good result for the performance metrics accuracy, recall, and precision evaluated. Under numerous aspects, our proposed approach produces superior outcomes on all of the applied dataset images. Furthermore, the experimental result achieves an average lung tumor stage classification accuracy of 97%-99.1% and an average of 98.6% which is significantly higher than the other existing techniques.

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Section: Proposed Methodologymentioning

confidence: 99%

Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques

Kasinathan¹,

Jayakumar²

2022

BioMed Research International

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“…It is conceivable that the results will not be improved even if the number of concealed layers is greatly increased. This is one of the possibilities [ 39 ]. Overfitting may also occur if a high number of layers are added all at once.…”

Section: Proposed Methodologymentioning

confidence: 99%

Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things

Joshi

Pandit²,

Shukla

et al. 2022

Computational Intelligence and Neuroscience

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The use of artificial intelligence (AI) and the Internet of Things (IoT), which is a developing technology in medical applications that assists physicians in making more informed decisions regarding patients’ courses of treatment, has become increasingly widespread in recent years in the field of healthcare. On the other hand, the number of PET scans that are being performed is rising, and radiologists are getting significantly overworked as a result. As a direct result of this, a novel approach that goes by the name “computer-aided diagnostics” is now being investigated as a potential method for reducing the tremendous workloads. A Smart Lung Tumor Detector and Stage Classifier (SLD-SC) is presented in this study as a hybrid technique for PET scans. This detector can identify the stage of a lung tumour. Following the development of the modified LSTM for the detection of lung tumours, the proposed SLD-SC went on to develop a Multilayer Convolutional Neural Network (M-CNN) for the classification of the various stages of lung cancer. This network was then modelled and validated utilising standard benchmark images. The suggested SLD-SC is now being evaluated on lung cancer pictures taken from patients with the disease. We observed that our recommended method gave good results when compared to other tactics that are currently being used in the literature. These findings were outstanding in terms of the performance metrics accuracy, recall, and precision that were assessed. As can be shown by the much better outcomes that were achieved with each of the test images that were used, our proposed method excels its rivals in a variety of respects. In addition to this, it achieves an average accuracy of 97 percent in the categorization of lung tumours, which is much higher than the accuracy achieved by the other approaches.

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Analysis of Lung Tumour Detection and Segmentation Using Level Set Method of Active Contour Model

Cited by 2 publications

References 7 publications

Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques

Cloud-Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques

Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things

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