Performance analyses of five neural network classifiers on nodule classification in lung CT images using WEKA: a comparative study.

In this report, we are presenting our work on performance analyses of five different neural network classifiers viz. MLP, DL4JMLP, logistic regression, SGD and simple logistic classifier in lung nodule detection using WEKA interface. To the best of our knowledge, this report demonstrates first use of WEKA for comparative performance analyses of neural network classifiers in identifying lung nodules from lung CT-images. A total of 624 handcrafted features from 52 numbers of lung CT-images collected randomly from Lung Image Database Consortium (LIDC) were fed into WEKA to evaluate the performances of the classifiers under four different categories of computation. Performances of the classifiers were observed in terms of 11 important parameters viz. accuracy, kappa statistic, root mean squared error, TPR, FPR, precision, sensitivity, F-measurement, MCC, ROC area and PRC area. Results show 86.53%, 77.77%, 55.55%, 94.44% & 88.88% accuracy as well as 0.91, 0.86, 0.68, 0.91 & 0.93 ROC area for MLP, DL4JMLP, logistic, SGD and simple logistic classifier respectively at tenfold cross-validation by taking 66% of the data set for training and 34% for testing and validation purpose. SGDClassifier has been found the best performing followed by simple logistic classifier for the purpose.

Follicular T-cell lymphoma.

Follicular T-cell lymphoma is a recently described, rare neoplasm with the true incidence still unknown. It is a lymph node-based tumor in which the node shows a follicular pattern of growth. Immunohistochemistry confirms the cells of origin to be follicular helper T-cells and thus plays an important role in the diagnosis of the tumor. We report a case of follicular variant of peripheral T-cell lymphoma, which was erroneously reported as Hodgkin lymphoma on fine-needle aspiration, and follicular lymphoma on hematoxylin and eosin.