3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation

Joshua, Eali Stephen Neal; Bhattacharyya, Debnath; Chakkravarthy, Midhun; Byun, Yung-Cheol

doi:10.1155/2021/6695518

Cited by 54 publications

(19 citation statements)

References 27 publications

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“…For lung cancer diagnosis, Joshua et al introduced the 3D CNN unsupervised learning model [ 15 ]. 3D CNN is a binary classifier model with an enhanced gradient activation function that improves lung tumor visibility.…”

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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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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“…For the experiments testing of the DB-NET proposed model, the approach we utilized is the benchmark dataset available on LUNA16 (Lung Nodule Analysis 2016) [ 46 ] grand challenge. LUNA16 is resulting from the “Lung Image Database Consortiums Images Collection (LIDC/IDRI).” Input folders have three main things; one is for the sample CT scan images with sample_1_images.…”

Section: Data and Experimentsmentioning

confidence: 99%

A bi-directional deep learning architecture for lung nodule semantic segmentation

et al. 2022

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Lung nodules are abnormal growths and lesions may exist. Both lungs may have nodules. Most lung nodules are harmless (not cancerous/malignant). Pulmonary nodules are rare in lung cancer. X-rays and CT scans identify the lung nodules. Doctors may term the growth a lung spot, coin lesion, or shadow. It is necessary to obtain properly computed tomography (CT) scans of the lungs to get an accurate diagnosis and a good estimate of the severity of lung cancer. This study aims to design and evaluate a deep learning (DL) algorithm for identifying pulmonary nodules (PNs) using the LUNA-16 dataset and examine the prevalence of PNs using DB-Net. The paper states that a new, resource-efficient deep learning architecture is called for, and it has been given the name of DB-NET. When a physician orders a CT scan, they need to employ an accurate and efficient lung nodule segmentation method because they need to detect lung cancer at an early stage. However, segmentation of lung nodules is a difficult task because of the nodules' characteristics on the CT image as well as the nodules' concealed shape, visual quality, and context. The DB-NET model architecture is presented as a resource-efficient deep learning solution for handling the challenge at hand in this paper. Furthermore, it incorporates the Mish nonlinearity function and the mask class weights to improve segmentation effectiveness. In addition to the LUNA-16 dataset, which contained 1200 lung nodules collected during the LUNA-16 test, the LUNA-16 dataset was extensively used to train and assess the proposed model. The DB-NET architecture surpasses the existing U-NET model by a dice coefficient index of 88.89%, and it also achieves a similar level of accuracy to that of human experts.

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“…According to their results, multitype dependency-based feature representations performed much better than single-type feature representations (accuracy of 75%, area under the curve = 0.78) when compared to traditional features that were extracted [ 18 ]. This strategy was developed in order to classify the various types of cancer that can be caused by tumour RNA sequences found in genomic data (CNN).…”

Section: Related Workmentioning

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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3D CNN with Visual Insights for Early Detection of Lung Cancer Using Gradient-Weighted Class Activation

Cited by 54 publications

References 27 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

A bi-directional deep learning architecture for lung nodule semantic segmentation

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

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