Lung cancer detection using image processing and deep learning

Asraa A. Abd Al-Ameer, Ghufran Abdulameer Hussien, Hajer. A. Al Ameri


This project is about the detection of lung cancer by training a model of deep neural networks using histopathological lung cancer tissue images. Deferent models have been proposed for detecting lung cancer cells automatically involving Inception V3, Random Forest, and convolutional neural network (CNN). The deep convolutional neural network has been trained to extract important features that facilitate build detection and diagnosis of lung cancer cells more efficiently and accurately. The proposed method in this project has accomplished promising and satisfactory results in terms of accuracy, precision, recall, F-score, and specificity measure in lung cancer detection. Furthermore, it has been applied on dataset which contains 178,000 photos. The accuracy values that are obtained are accuracy 97.09%, precision 96.89%, recall 97.31%, F-score measure 97.09%, and specificity measure 96.88%.


Classification; Deep learning; Histopathology; Image compression; Image processing; Lung cancer detection

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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