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Deep learning for lung lesion detection
A. Işın,
Published in Springer Verlag
Volume: 896
Pages: 799 - 806
As the most fatal cancer type, early diagnosis of the lung cancer plays an important role for the survival of the patients. Diagnosis of the lung cancer involves screening the patients initially by Computed Tomography (CT) for the presence of lung lesions. This procedure requires expert radiologists which need to go over very large numbers of image slices manually in order to detect and diagnose lung lesions. Unfortunately this is a very time consuming process and its performance is very dependent on the performing radiologist. Thus assisting the radiologists by developing an automated computer aided detection (CAD) system is an interesting research goal. In this regard, as the aim of this paper a pre-trained AlexNet (deep learning) framework is transferred to develop and implement a robust CAD system for the classification of lung images depending on whether they bear a lung lesion or not. High performances of 98.72% sensitivity, 98.35% specificity and 98.48% accuracy are reported as a result. © 2019, Springer Nature Switzerland AG.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing
PublisherData powered by TypesetSpringer Verlag