Medical images analysis is of great importance for early and accurate diagnosis of pulmonary disease and assist medical doctors for effective treatments and prevents further deaths. This model aims to identify and classify lung Computerized Tomography (CT) scan images as healthy lungs and for diseases like COPD and Fibrosis. The proposed feature selection methods are suitable for classification of diseases in medical images, can also be used in real-time applications due to their reduced computational cost and very high accuracy. Three steps are required to achieve these goals: Extracting relevant features from the lung images, Feature Selection and Identification of lung diseases using a machine learning classifier. In the first step, model extracts Haralick texture features using Gray Level Co-occurrence Matrix, Zernike’s moments, Gabor features and Tamura texture features from the segmented lung images to compose a pool of features for selection. In the second step, three evolutionary algorithms, Improvised Crow Search Algorithm (ICSA), Improvised Grey Wolf Algorithm (IGWA) and Improvised Cuttlefish Algorithm (ICFA), as a feature selection methods, which selects an optimal features subset from a large pool of features extracted from medical images to improve the classification accuracy and reduce the computational costs. In the third step ML models were used.
Input variables : Lung CT scan images
Output Variables : Status of the patient - COPD, Fibrosis, Healthy
Visit Model : github.com
Additional links : sciencedirect.com
| Model Category | : | Public |
| Date Published | : | September, 2020 |
| Healthcare Domain | : | Provider |
| Code | : | github.com |
| Health Risk Management |
| Disease Detection |