Background: Neural Networks are utilized in several applications in the
field of healthcare, one such being the classification of lung cancers. Innovative
advancements in diagnosing tumours are a major boost to developing novel treatment
techniques in the early stages of lung cancer.
Method: In this work, a novel image-based features selection method for classifying
lung Computed Tomography (CT) images is introduced. A new fusion-based
technique through a combination of Gabor filters and first order histograms was
created. The suggested model utilizes Multi-Layer Perceptron Neural Networks
(MLP-NN) alongside Krill Herds (KH) for structural optimization which consists of
three phases. First, images are pre-processed and features selected through the new
fusion-based selection method. Next, the selected features are got through the application of Correlation
based Feature Selection (CFS), Mutual Information (MI), Fuzzy Unordered Rule Induction Algorithm
(FURIA) that choose the highest ranked ones. Lastly, classifiers like AdaBboost or MLP-NN carry out
the classification of the cancers.
Results: Misclassification rates, average true positive rates, average false discovery rates are utilized in
the evaluation of CFS-Furia, CFS-AdaBoost classifier, CFS-MLPNN, CFS-KHMLPNN, MI-Furia, MIAdaBoost
classifier, MI-MLPNN and MI-KHMLPNN. The suggested MI-KHMLPNN outperforms all
others in every category.
Conclusion: The model was evaluated with several lung CT images and has proven to attain excellent
results in the classification of lung cancers.