Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection

• Published in: Pattern recognition letters 2020-01, Vol.129, p.77-85
• Main Authors: Khan, M. Attique, Rubab, S., Kashif, Asifa, Sharif, Muhammad Imran, Muhammad, Nazeer, Shah, Jamal Hussain, Zhang, Yu-Dong, Satapathy, Suresh Chandra
• Format: Article
• Language: English
• Subjects: Cancer; Classification; Computed tomography; Contrast normalization; Entropy; Feature extraction; Fusion; Image classification; Image contrast; Low visibility; Lung cancer; Lungs; Lungs cancer; Medical imaging; Multiple features; Selection; Tumors
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2019.11.014

•Contrast is enhance through gamma correction max intensity weights (GCmIW) approach.•Multiple features are fused through a serially canonical correlation-based approach.•Zero features are removed through entropy value.•Weighted neighborhood component analysis based features selected. Lung cancer is a fatal type of cancer and it causes of severe deaths of approximately 422 people every day, worldwide. However, an early diagnosis is an expedient requirement for increasing the chances of human survival. In this regard the existing techniques of tumor detection, CT scans are mostly utilized to recognize the infected regions, nevertheless, the major challenges of CT images are low visibility of tumor regions, negative rates, to name but a few. In this work, we propose a novel design of contrast stretching based classical features fusion process for localizing the of lungs cancer classification. The proposed method encompasses three significant steps: firstly, contrast of original CT images is improved by gamma correction max intensity weights approach. Secondly, multiple texture, point, and geometric features are extracted from contrast images, which are later subjected to a serial canonical correlation-based fusion. Thirdly, zero values and negative features are replaced by an entropy-based approach, followed by weighted NCA for selection. Most discriminate features are fed into ensemble classifier for final classification. The validation of the proposed method is conducted on publicly available dataset: Lungs Data Science Bowl 2017 to achieving maximum accuracy of 99.4%. The numerical results clearly show that the performance of our proposed method outperforms in comparison with several existing methods with greater accuracy.