• 2022-09
  • 2022-08
  • 2022-07
  • 2022-05
  • 2022-04
  • 2021-03
  • 2020-08
  • 2020-07
  • 2018-07
  • D-Luciferin br Santanu Ghorai Anirban Mukherjee


    [3] Santanu Ghorai, Anirban Mukherjee, Sanghamitra Sengupta, Pranab K. Dutta, Cancer classification from gene expression data by NPPC ensemble, IEEE/ACM Trans. Comput. Biol. Bioinform. 8 (3) (2011) 659–671.
    [5] Valdigleis S. Costaa, Antonio Diego S. Fariasa, Benjamín Bedregala, Re-givan H.N. Santiagoa, AnneMagaly de P. Canutoa, Combining multiple algorithms in classifier ensembles usinggeneralized mixture functions, Neuro Computing 313 (2018) 402–414. [6] Shamsul Huda, John Yearwood, Herbert F. Jelinek, Mohammad Mehedi Hassan, Giancarlo Fortino, Michael Buckland, A hybrid feature selection with ensemble classification for imbalanced healthcare data: A case study for D-Luciferin tumor diagnosis, IEEE Access 4 (2016) 9145–9154. [7] Sarfaraz Hussein, Pujan Kandel, Candice W. Bolan, Michael B. Wallace, Ulas Bagci, Supervised and unsupervised tumor characterization in the deep learning era, IEEE Trans. Med. Imaging 2 (2018) 1–11. [8] Emmanuel Adetiba, Oludayo O. Olugbara, Improved classification of lung Cancer using radial basis function neural network with affine transforms of voss representation, PLoS One J. 10 (12) (2015) 1–25. [9] Divya Jain, Vijendra Singh, Feature selection and classification systems for chronic disease prediction: A review, Egyptian Inf. J. 19 (3) (2018) 179–189. [10] Payal Dande, Purva Samant, Acquaintance to artificial neural networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review, Tuberculosis 108 (2018) 1–9. [11] Pegah Khosravia, Ehsan Kazemic, Marcin Imielinskid, Olivier Elemento, Iman Hajirasouliha, Deep convolutional neural networks enable discrimi-nation of heterogeneous digital pathology images, EBioMedicine 27 (2018) 317–328.
    [12] Ayman El-Baz, Garth M. Beache, Georgy Gimel’farb, Kenji Suzuki, Kazunori Okada, Ahmed Elnakib, Ahmed Soliman, Behnoush Abdollahi, Computer-aided diagnosis systems for lung Cancer: Challenges and methodologies, Int. J. Biomed. Imaging 2013 (2012) 1–46. [13] Faezeh Hosseinzadeh, Amir Hossein KayvanJoo, Mansuor Ebrahimi, Bahram Goliaei, Prediction of lung tumor types based on protein attributes by machine learning algorithms, Springer Plus 2 (238) (2013) 1–14. [14] Ashutosh Kumar Dubey, Umesh Gupta, Sonal Jain, Epidemiology of lung cancer and approaches for its prediction: a systematic review and analysis, Chin. J. Cancer 35 (71) (2016) 1–13. [15] Maxim D. Podolsky, Anton A. Barchuk, Vladimir I. Kuznetcov, Natalia F. Gusarova, Vadim S. Gaidukov, Segrey A. Tarakanov, Evaluation of machine learning algorithm utilization for lung Cancer classification based on gene expression levels, Asian Pac. J. Cancer Prevent. 17 (2) (2016) 835–838.  [16] Zhiguo Zhou, Zhi-Jie Zhou, Hongxia Hao, Shulong Li, Xi Chen, You Zhang, Michael Folkert, Jing Wang, Constructing multi-modality and multiclassifier radiomics predictive models through reliable classifier fusion, IEEE Comput. Soc. (2017) 1–13. [17] Kui Liu, Guixia Kang, Multiview convolutional neural networks for lung nodule classification, Int. J. Imaging Syst. Technol. 27 (1) (2017) 12–22. [18] Sahil Sharma, Vinod Sharma, Atul Sharma, A two stage hybrid ensemble classifier based diagnostic tool for chronic kidney disease diagnosis using optimally selected reduced feature set, Int. J. Intell. Syst. Appl. Eng. 6 (2) (2018) 113–122. [19] Mohamad Rabbani, Jonathan Kanevsky, Kamran Kafi, Florent Chandelier, Francis J. Giles, Role of artificial intelligence in the care of patients with nonsmall cell lung cancer, Eur. J. Clin. Invest. 48 (4) (2018) 1–7. [20] Thangavel Baranidharan, Thangavel Sumathi, Vadivelraj Chandra Shekar, Weight optimized neural network using metaheuristics for the classifica-tion of large cell Carcinoma and adenocarcinoma from lung imaging, Curr. Signal Transduct. Therapy 11 (2) (2016) 91–97. [21] Changmiao Wang, Ahmed Elazab, Jianhuang Wu, Qingmao Hu, Lung nodule classification using deep feature fusion in chest radiography, Comput. Med. Imaging Graph. 57 (2017) 10–18.