How Deep Learning Is Changing the Landscape of Lung Cancer Diagnosis

Cancer is one of the diseases with high mortality in the 21st century, and lung cancer ranks first in all cancer morbidity and mortality. In recent years, with the rise of big data and artificial intelligence, lung cancer-assisted diagnosis based on deep learning has gradually become A popular research topic. Computer-aided lung cancer diagnosis technology is mainly the process of processing and analyzing the lung image data obtained by medical instrument imaging. The process is summarized into four steps: medical image data preprocessing, lung parenchymal segmentation, lung Nodule detection and segmentation, as well as lesion diagnosis. In order to solve the problem that the two-dimensional image model is not applicable to three-dimensional images, this paper proposes a three-dimensional convolutional neural network model suitable for lung cancer diagnosis. The model consists of two parts. The first part is a three-dimensional deep nodule detection network (FCN) model, which generates a heat map of the lung nodules. We can locate the locations of those malignant nodules through the heat map. According to the heat map generated in the first part, the second part selects those malignant nodules that are likely to be large, and then fuses the features of these selected nodules into one feature vector, showing the whole lung scan. Finally, we use this feature to classify and determine whether we have lung cancer.

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A Review of Deep Learning Techniques Applied in Lung Cancer Diagnosis

Lecture Notes in Electrical Engineering - Signal and Information Processing, Networking and Computers ◽

10.1007/978-981-15-4163-6_95 ◽

2020 ◽

pp. 800-807

Author(s):

Thamali Madhushani Adhikari ◽

Haylee Liska ◽

Zhaoyang Sun ◽

Yan Wu

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Cancer Diagnosis ◽

Lung Cancer Diagnosis ◽

Learning Techniques

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Imaging-based Deep Learning for Lung Cancer Diagnosis and Staging

Case Medical Research ◽

10.31525/ct1-nct04000620 ◽

2019 ◽

Author(s):

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Cancer Diagnosis ◽

Lung Cancer Diagnosis

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Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes

Applied Sciences ◽

10.3390/app11020865 ◽

2021 ◽

Vol 11 (2) ◽

pp. 865

Author(s):

Oswaldo Solarte Pabón ◽

Maria Torrente ◽

Mariano Provencio ◽

Alejandro Rodríguez-Gonzalez ◽

Ernestina Menasalvas

Keyword(s):

Lung Cancer ◽

Deep Learning ◽

Cancer Diagnosis ◽

Hybrid Approach ◽

Named Entity Recognition ◽

Entity Recognition ◽

Clinical Notes ◽

Named Entity ◽

Lung Cancer Diagnosis ◽

Diagnosis Date

Despite efforts to develop models for extracting medical concepts from clinical notes, there are still some challenges in particular to be able to relate concepts to dates. The high number of clinical notes written for each single patient, the use of negation, speculation, and different date formats cause ambiguity that has to be solved to reconstruct the patient’s natural history. In this paper, we concentrate on extracting from clinical narratives the cancer diagnosis and relating it to the diagnosis date. To address this challenge, a hybrid approach that combines deep learning-based and rule-based methods is proposed. The approach integrates three steps: (i) lung cancer named entity recognition, (ii) negation and speculation detection, and (iii) relating the cancer diagnosis to a valid date. In particular, we apply the proposed approach to extract the lung cancer diagnosis and its diagnosis date from clinical narratives written in Spanish. Results obtained show an F-score of 90% in the named entity recognition task, and a 89% F-score in the task of relating the cancer diagnosis to the diagnosis date. Our findings suggest that speculation detection is together with negation detection a key component to properly extract cancer diagnosis from clinical notes.

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