ExtRanFS: An Automated Lung Cancer Malignancy Detection System Using Extremely Randomized Feature Selector

Abstract

Lung cancer is an abnormality where the body’s cells multiply uncontrollably. The disease can be deadly if not detected in the initial stage. To address this issue, an automated lung cancer malignancy detection (ExtRanFS) framework is developed using transfer learning. We used the IQ-OTH/NCCD dataset gathered from the Iraq Hospital in 2019, encompassing CT scans of patients suffering from various lung cancers and healthy subjects. The annotated dataset consists of CT slices from 110 patients, of which 40 were diagnosed with malignant tumors and 15 with benign tumors. Fifty-five patients were determined to be in good health. All CT images are in DICOM format with a 1mm slice thickness, consisting of 80 to 200 slices at various sides and angles. The proposed system utilized a convolution-based pre-trained VGG16 model as the feature extractor and an Extremely Randomized Tree Classifier as the feature selector. The selected features are fed to the Multi-Layer Perceptron (MLP) Classifier for detecting whether the lung cancer is benign, malignant, or normal. The accuracy, sensitivity, and F1-Score of the proposed framework are 99.09%, 98.33%, and 98.33%, respectively. To evaluate the proposed model, a comparison is performed with other pre-trained models as feature extractors and also with the existing state-of-the-art methodologies as classifiers. From the experimental results, it is evident that the proposed framework outperformed other existing methodologies. This work would be beneficial to both the practitioners and the patients in identifying whether the tumor is benign, malignant, or normal.

Keywords

• lung cancer malignancy detection
• transfer learning
• feature extraction
• feature selection
• extratree classifier
• deep learning