Lung cancer medical images classification using hybrid CNN-SVM

Abstract

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.

Development of hybrid convolutional neural network and autoregressive integrated moving average on computed tomography image classification

“…Agarwal et al [10] proposed a method to classify lung cancers as either malignant or benign by combining a CNN with the AlexNet network model whereas Ardimento et al [17] suggested a novel ensemble-based method for classifying lung cancer more accurately using CT scan images. Saleh et al [20] developed an approach for classifying CT images of the lung using a hybrid of CNN and SVM algorithms. The goal was to effectively detect the presence of cancer cells.…”

Comparison of Convolution Neural Network Architecture for Colon Cancer Classification

“…In most cases of classification and detection, convolutional neural network shows significant performance. Recently, CNN classification of lung cancer based on computer tomography (CT) image has been reported in [17]- [20]. Study by Saleh [17], combined CNN-SVM in CT image-based lung cancer detection and succeeded in generating 97.91% accuracy.…”

“…Recently, CNN classification of lung cancer based on computer tomography (CT) image has been reported in [17]- [20]. Study by Saleh [17], combined CNN-SVM in CT image-based lung cancer detection and succeeded in generating 97.91% accuracy. Meanwhile, similar classification cases have been reported in [18], [19], generate 93.54% and 100% accuracy respectively.…”

Robusta coffee leaf diseases detection based on MobileNetV2 model

“…6, December 2022: 6675-6683 6676 more efficient method of identifying plant diseases [8], [9]. Deep learning architecture, namely convolutional neural network (CNN), has shown remarkable performance in image classification [10]- [12]. The CNN model was used to diagnose diseases on tomato leaves in the most recent research, which yielded an accuracy score of 95% with a batch size of 16 and a total loss of 0.1265 utilizing only 4,671 images of tomato leaves.…”