DETECTING LUNG CANCER FROM CT IMAGES FOR LUNG CANCER DIAGNOSIS
Keywords:
Lung Cancer Diagnostics, Deep Learning, Chest X-Ray, Magnetic Resonance Imaging,Abstract
Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. Therefore, this work implements the advanced modifications in CNN model for the detection of lung cancer from chest scan images. The proposed CNN model is able to classify the benign and malignant i.e., normal, and cancerous with higher accuracy as compared to state-of-the-art machine learning approach called support vector machine (SVM) classifier. In addition, the obtained quality metrics discloses the superiority of proposed deep CNN model for assisting the expertise in an enhanced diagnosis.
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References
Y. Xu, A. Hosny, R. Zeleznik, C. Parmar, T. Coroller, I. Franco, H.J. Aerts, Deep learning predicts
lung cancer treatment response from serial medical imaging, Clin. Cancer Res. 25 (11) (2019)
–3275.
M.I. Faisal, S. Bashir, Z.S. Khan, F.H. Khan, An evaluation of machine learning classifiers and
ensembles for early stage prediction of lung cancer, December, in: 2018 3rd International
Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST), IEEE,
, pp. 1–4.
N. Coudray, P.S. Ocampo, T. Sakellaropoulos, N. Narula, M. Snuderl, D. Feny¨o, A. Tsirigos,
Classification and mutation prediction from non–small cell lung cancer histopathology images
using deep learning, Nat. Med. 24 (10) (2018) 1559–1567.
D.M. Ibrahim, N.M. Elshennawy, A.M. Sarhan, Deep-chest: multi-classification deep learning
model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases, Comput. Biol.
Med. 132 (2021), 104348.
Avanzo, J. Stancanello, G. Pirrone, G. Sartor, Radiomics and deep learning in lung cancer,
Strahlenther. Onkol. 196 (10) (2020) 879–887.
S.H. Hyun, M.S. Ahn, Y.W. Koh, S.J. Lee, A machine-learning approach using PET- based
radiomics to predict the histological subtypes of lung cancer, Clin. Nucl. Med. 44 (12) (2019)
–960.
P. R. Radhika, R.A. Nair, G. Veena, A comparative study of lung cancer detection using machine
learning algorithms, 2019, February, in: IEEE International Conference on Electrical, Computer
and Communication Technologies (ICECCT), IEEE, 2019, pp. 1–4.
K. Pradhan, P. Chawla, Medical Internet of things using machine learning algorithms for lung
cancer detection, J. Manag. Anal. 7 (4) (2020) 591–623.
S. Bhatia, Y. Sinha, L. Goel, Lung cancer detection: a deep learning approach, in: Soft Computing
for Problem Solving, Springer, Singapore, 2019, pp. 699–705.
H. Shin, S. Oh, S. Hong, M. Kang, D. Kang, Y.G. Ji, Y. Choi, Early-stage lung cancer diagnosis
by deep learning-based spectroscopic analysis of circulating exosomes, ACS Nano 14 (5) (2020)
–5444.
V. Rajasekar, B. Predi´c, M. Saracevic, M. Elhoseny, D. Karabasevic, D. Stanujkic, P. Jayapaul,
Enhanced multimodal biometric recognition approach for smart cities based on an optimized
fuzzy genetic algorithm, Sci. Rep. 12 (1) (2022) 1–11.
S.H. Hyun, M.S. Ahn, Y.W. Koh, S.J. Lee, A machine-learning approach using PET- based
radiomics to predict the histological subtypes of lung cancer, Clin. Nucl. Med. 44 (12) (2019)
–960.
Y. She, Z. Jin, J. Wu, J. Deng, L. Zhang, H. Su, C. Chen, Development and validation of a deep
learning model for non–small cell lung cancer survival, JAMA Netw. Open 3 (6) (2020) e205842,
e205842.
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