Deep Learning Based Lung Cancer Detection and Classification

Kalaivani, N.; Manimaran, Natraj; Sophia, S.; Devi, D.

doi:10.1088/1757-899x/994/1/012026

Cited by 59 publications

(18 citation statements)

References 3 publications

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“…Connected cells and gates form the LSTM's memory block, which is made up of the LSTM's building blocks. These gateways and cells are used rather often for the goals of retaining input states and updating [ 27 ]. Table 3 represents the LSTM features [ 28 ].…”

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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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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“…These are the most frequent and typical threshold CT data used for disease diagnosis, according to the authors in [ 9 ]. Kalaivani et al introduced the DenseNet model, a binary classifier based on a deep CNN network for detecting malignant or benign lung cancer patients [ 17 ]. The researchers employed a dataset of 201 lung scans, with 85 percent of the photos being used for training and 15 percent being used for testing and classification.…”

Section: Related Workmentioning

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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“…Accuracy of 95.91% was achieved using a probabilistic neural network (PNN) by extracting lung volume, and reduction was done using principal component analysis (PCA) [ 38 ]. Accuracy of 95.62% was achieved using texture, volumetric, intensity, and geometric features, and Fuzzy Particle Swarm Optimization (FPSO) was used for feature selection, with deep learning being applied for classification [ 39 ]. Sensitivity of 93.02% was achieved in detection detecting ground-glass opacity (GGO) using Support Vector Machine (SVM) twice and using four 2-dimensional features and 11 3-dimensional features [ 40 ].…”

Section: Related Workmentioning

confidence: 99%

Ensemble Learning Framework with GLCM Texture Extraction for Early Detection of Lung Cancer on CT Images

Althubiti

Paul

Mohanty

et al. 2022

Computational and Mathematical Methods in Medicine

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Lung cancer has emerged as a major cause of death among all demographics worldwide, largely caused by a proliferation of smoking habits. However, early detection and diagnosis of lung cancer through technological improvements can save the lives of millions of individuals affected globally. Computerized tomography (CT) scan imaging is a proven and popular technique in the medical field, but diagnosing cancer with only CT scans is a difficult task even for doctors and experts. This is why computer-assisted diagnosis has revolutionized disease diagnosis, especially cancer detection. This study looks at 20 CT scan images of lungs. In a preprocessing step, we chose the best filter to be applied to medical CT images between median, Gaussian, 2D convolution, and mean. From there, it was established that the median filter is the most appropriate. Next, we improved image contrast by applying adaptive histogram equalization. Finally, the preprocessed image with better quality is subjected to two optimization algorithms, fuzzy c-means and k-means clustering. The performance of these algorithms was then compared. Fuzzy c-means showed the highest accuracy of 98%. The feature was extracted using Gray Level Cooccurrence Matrix (GLCM). In classification, a comparison between three algorithms—bagging, gradient boosting, and ensemble (SVM, MLPNN, DT, logistic regression, and KNN)—was performed. Gradient boosting performed the best among these three, having an accuracy of 90.9%.

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Deep Learning Based Lung Cancer Detection and Classification

Cited by 59 publications

References 3 publications

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

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

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

Ensemble Learning Framework with GLCM Texture Extraction for Early Detection of Lung Cancer on CT Images

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