A Novel Support Vector Machine Classifier Using Soft Computing Approach for Automated Classification of Emphysema, Bronchiectasis and Pleural Effusion Using Optimized Gabor Filter

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 provider. 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 classifier. 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.