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

Kasinathan, Gopi; Jayakumar, S.

doi:10.1155/2022/4185835

Cited by 46 publications

(15 citation statements)

References 36 publications

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“…Of the included studies, 869 used a classification model in a part of their pipelines. Of these, 190 studies combined classification with other types of ML tasks, for example, first segmenting a lung nodule and then classifying the isolated nodule as benign or malignant [ 53 ].…”

Section: Results and Synthesismentioning

confidence: 99%

Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

et al. 2022

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Machine-learning (ML) and deep-learning (DL) algorithms are part of a group of modeling algorithms that grasp the hidden patterns in data based on a training process, enabling them to extract complex information from the input data. In the past decade, these algorithms have been increasingly used for image processing, specifically in the medical domain. Cardiothoracic imaging is one of the early adopters of ML/DL research, and the COVID-19 pandemic resulted in more research focus on the feasibility and applications of ML/DL in cardiothoracic imaging. In this scoping review, we systematically searched available peer-reviewed medical literature on cardiothoracic imaging and quantitatively extracted key data elements in order to get a big picture of how ML/DL have been used in the rapidly evolving cardiothoracic imaging field. During this report, we provide insights on different applications of ML/DL and some nuances pertaining to this specific field of research. Finally, we provide general suggestions on how researchers can make their research more than just a proof-of-concept and move toward clinical adoption.

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Section: Results and Synthesismentioning

confidence: 99%

Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

et al. 2022

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“…The VGG CNN model's flaw is that it hasn't been preprocessed for background subtraction or image reconstruction fragmentation, which increases the predictive accuracy. In Kasinathan and Jayakumar (32), the new cloud-based tumor recognition model was developed. The author analyzed various standard dataset "CT-scans and PET-scans" for segmenting the ROC and for recognizing the tumor.…”

Section: Related Workmentioning

confidence: 99%

Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks

et al. 2022

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Lung cancer is the cellular fission of abnormal cells inside the lungs that leads to 72% of total deaths worldwide. Lung cancer are also recognized to be one of the leading causes of mortality, with a chance of survival of only 19%. Tumors can be diagnosed using a variety of procedures, including X-rays, CT scans, biopsies, and PET-CT scans. From the above techniques, Computer Tomography (CT) scan technique is considered to be one of the most powerful tools for an early diagnosis of lung cancers. Recently, machine and deep learning algorithms have picked up peak energy, and this aids in building a strong diagnosis and prediction system using CT scan images. But achieving the best performances in diagnosis still remains on the darker side of the research. To solve this problem, this paper proposes novel saliency-based capsule networks for better segmentation and employs the optimized pre-trained transfer learning for the better prediction of lung cancers from the input CT images. The integration of capsule-based saliency segmentation leads to the reduction and eventually reduces the risk of computational complexity and overfitting problem. Additionally, hyperparameters of pretrained networks are tuned by the whale optimization algorithm to improve the prediction accuracy by sacrificing the complexity. The extensive experimentation carried out using the LUNA-16 and LIDC Lung Image datasets and various performance metrics such as accuracy, precision, recall, specificity, and F1-score are evaluated and analyzed. Experimental results demonstrate that the proposed framework has achieved the peak performance of 98.5% accuracy, 99.0% precision, 98.8% recall, and 99.1% F1-score and outperformed the DenseNet, AlexNet, Resnets-50, Resnets-100, VGG-16, and Inception models.

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“…Kasinathan et al 28 (2022) proposed a hybrid approach for PET/CT images called cloud‐based lung tumor detector and stage classifier (Cloud‐LTDSC). The technique's performance was examined using an LIDC‐IDRI dataset of 50 low doses and lung CT DICOM images.…”

Section: Related Workmentioning

confidence: 99%

A hybrid machine learning technique for early prediction of lung nodules from medical images using a learning‐based neural network classifier

Musthafa¹,

Sankar

Benil

et al. 2022

Concurrency and Computation

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Lung cancer is one of the major causes of death in the world, according to radiologists. However, a constant flow of medical images to hospitals is forcing radiologists to focus on accurate early prediction of nodules. Recently, several image-processing techniques have cooperated for the early prediction of lung nodules. However, it's hard to detect strong nodes because of lung node diversity and environmental complexity. This study presents a hybrid machine learning technique for predicting an early prognosis of lung nodules from clinical images using a learning-based neural network classifier.First, we introduce an improved snake swarm optimization with a bat model (ISSO-B) for lung nodule segmentation using statistical information. Second, we demonstrate a chaotic atom search optimization (CASO) algorithm to select the optimal best features among multiple features, which minimize the dimensionality problem. Third, we develop a hybrid learning-based deep neural network classifier (L-DNN) for nodule prediction and classification. Finally, we evaluate our proposed technique with different public datasets LIDC-IDRI and FAH-GMU. Then, performance can be compared with the latest technology in terms of accuracy, sensitivity, specificity, and area under curve (AUC).

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

Cited by 46 publications

References 36 publications

Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review

Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks

A hybrid machine learning technique for early prediction of lung nodules from medical images using a learning‐based neural network classifier

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