Intelligent Diagnostic System for Nuclei Structure Classification of Thyroid Cancerous and Non-Cancerous Tissues

Recently, image mining has opened new bottlenecks in the field of biomedical discoveries and machine leaning techniques have brought significant revolution in medical diagnosis. Especially, classification problem of human cancerous tissues would assume to be one of the really challenging problems since it requires very high optimized algorithms to select the appropriate features from histopathological images of well-differentiated thyroid cancers. For instance prediction of initial changes in neoplasm such as hidden patterns of nuclei overlapping sequences, variations in nuclei structures, distortion in chromatin distributions and identification of other micro- architectural behaviors would provide more meticulous assistance to doctors in early diagnosis of cancer. In-order to mitigate all above stated problems this paper proposes a novel methodology so called “Intelligent Diagnostic System for Nuclei Structural Classification of Thyroid Cancerous and Non-Cancerous Tissues” which classifies nuclei structures and cancerous behaviors from medical images by using proposed algorithm Auto_Tissue_Analysis. Overall methodology of approach is comprised of four layers. In first layer noise reduction techniques are used. In second layer feature selection techniques are used. In third layer decision model is constructed by using random forest (tree based) algorithm. Finally result visualization and performance evaluation is done by using confusion matrix, precision and recall measures. The overall classification accuracy is measured about 74% with 10-k fold cross validation.

@article{Chandio2017,
  title     = {Intelligent Diagnostic System for Nuclei Structure Classification of Thyroid Cancerous and Non-Cancerous Tissues},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {7},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Jamil Ahmed Chandio and M. Abdul Rehman Soomrani},
  doi       = {10.14569/IJACSA.2017.080746},
  url       = {https://doi.org/10.14569/IJACSA.2017.080746}
}