Dual-Model Approach for Lung Disease Classification Using Convolutional Neural Networks and Support Vector Machines

This paper proposes a solution of integrating the use of Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) for categorizing lung conditions of people with and without lung disease. The proposed model has achieved a high F1-Score of 97.4 %, a high Precision of 93.17% and high Recall of 93. 63 for the Healthy class. The F1 Scores of this study for the Infected Lung class were as follows: the accuracy of F1 Score was 97.4%; Precision was 94.12%; and Recall was 92.06%. The combining of SVM and CNN model was quite accurate with the accuracy of 97% and the model was accurate in multiple domains of assessment. These results suggest that the application of CNNs and SVMs to the task of medical image analysis constitutes a promising and accurate approach for diagnosing lung diseases.