Imran Arshad Choudhry*, Adnan N. Qureshi
CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1445-1463, 2022, DOI:10.32604/cmc.2022.025208
Abstract The well-established mortality rates due to lung cancers, scarcity of radiology experts and inter-observer variability underpin the dire need for robust and accurate computer aided diagnostics to provide a second opinion. To this end, we propose a feature grafting approach to classify lung cancer images from publicly available National Institute of Health (NIH) chest X-Ray dataset comprised of 30,805 unique patients. The performance of transfer learning with pre-trained VGG and Inception models is evaluated in comparison against manually extracted radiomics features added to convolutional neural network using custom layer. For classification with both approaches, Support… More >
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