Background: Automated Medical Image Analysis has emerged as an
important tool for the diagnoses of anatomical pathology and can be integrated with
the medical information system to deliver useful information for the health care
Method: This study proposes a novel SVM Classifier Using Soft Computing Approach
for Automated Classification of Emphysema, Bronchiectasis and Pleural Effusion
Using Optimized Gabor Filter. In this work an improved Gabor Filter using Firefly
optimization algorithm to find the optimal Gabor parameters is proposed. The proposed
technique is used to extract features from lung CT images and classify automatically
the images as Normal, Emphysema, Bronchiectasis and Pleural Effusion using SVM
Results: 40 CT images of Emphysema, Bronchiectasis, pleural effusion and 100 CT images of normal are
used. The performance of classification accuracy, average PPV, average sensitivity, and average f measure is
used for evaluating all the techniques. The proposed Optimized Gabor Filter -SVM displays best
performance in all categories.
Conclusion: The proposed method was tested with a number of CT lung images and satisfactory results
was achieved in classifying the lung diseases.