The growing size and number of the medical images necessitated the use of computers to facilitate... more The growing size and number of the medical images necessitated the use of computers to facilitate processing and analysis. In medical world, diagnostic imaging is an invaluable and important tool for early detection of diseases. In this project, we propose a novel classification method in low dose computed tomography scans. In the proposed system, first, the images are preprocessed and features are extracted. Second, for feature selection, t test method is applied. This selects the significant features. These features were optimized and the resultant set was used for classification of lung nodules into four categories: juxtapleural, well-circumscribed, vascularized and pleural-tail, based on the extracted information. Finally, Decision trees classifier are utilized for classification and overall detection performance is evaluated. The system has been tested with a number of real Computed Tomography lung images and has achieved satisfactory results in classifying the lung diseases. The results show that the proposed method achieves higher classification performance than traditional methods.
The growing size and number of the medical images necessitated the use of computers to facilitate... more The growing size and number of the medical images necessitated the use of computers to facilitate processing and analysis. In medical world, diagnostic imaging is an invaluable and important tool for early detection of diseases. In this project, we propose a novel classification method in low dose computed tomography scans. In the proposed system, first, the images are preprocessed and features are extracted. Second, for feature selection, t test method is applied. This selects the significant features. These features were optimized and the resultant set was used for classification of lung nodules into four categories: juxtapleural, well-circumscribed, vascularized and pleural-tail, based on the extracted information. Finally, Decision trees classifier are utilized for classification and overall detection performance is evaluated. The system has been tested with a number of real Computed Tomography lung images and has achieved satisfactory results in classifying the lung diseases. The results show that the proposed method achieves higher classification performance than traditional methods.
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Papers by Abina Rijeesh