Automatic Lung Segmentation in CT Images Using Mask R-CNN for Mapping the Feature Extraction in Supervised Methods of Machine Learning

Souza, Luís Fabrício de F.; Holanda, Gabriel B.; Alves, Shara S. A.; Silva, Francisco Hércules dos S.; Filho, Pedro Pedrosa Rebouças

doi:10.1007/978-3-030-49342-4_14

Cited by 1 publication

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“…The authors addressed the challenges on designing the RNN model for lung tumor detection in terms of localization [ 12 ]. Lusfabricio et al [ 13 ] created a mask RNN model that goes through the lung segmentation process to build a respiratory map and then uses fine-tuning to locate the border of pulmonary nodules on the DICOM CT lung image. The limitations of the existing lung cancer detection methods are inefficient for analyzing the large-scale database which leads to high performance in accuracy rate, F1-score, sensitivity, recall, and precision metrics.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%