Lung cancer medical images classification using hybrid CNN-SVM

Abdulrazak Yahya Saleh (Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak)
Chee Ka Chin (Faculty of Engineering, Universiti Malaysia Sarawak)
Vanessa Penshie (Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak)
Hamada Rasheed Hassan Al-Absi (College of Science and Engineering, Hamad Bin Khalifa University)

International Journal of Advances in Intelligent Informatics
Vol 7, No 2 (2021): July 2021

Lung cancer is one of the leading causes of death worldwide. Early detection of this disease increases the chances of survival. Computer-Aided Detection (CAD) has been used to process CT images of the lung to determine whether an image has traces of cancer. This paper presents an image classification method based on the hybrid Convolutional Neural Network (CNN) algorithm and Support Vector Machine (SVM). This algorithm is capable of automatically classifying and analyzing each lung image to check if there is any presence of cancer cells or not. CNN is easier to train and has fewer parameters compared to a fully connected network with the same number of hidden units. Moreover, SVM has been utilized to eliminate useless information that affects accuracy negatively. In recent years, Convolutional Neural Networks (CNNs) have achieved excellent performance in many computer visions tasks. In this study, the performance of this algorithm is evaluated, and the results indicated that our proposed CNN-SVM algorithm has been succeed in classifying lung images with 97.91% accuracy. This has shown the method's merit and its ability to classify lung cancer in CT images accurately.