Lung and Tumor Characterization in the Machine Learning Era
R. Subalakshmi1, G. Baskar2
1R.Subalakshmi*, Assistant Professor, Department of Information Technology, PSG College of Arts & Science Coimbatore (Tamil Nadu), India.
2Dr. G. Baskar, Assistant Professor, Department of Computer Science, K.S.G College of Arts & Science Coimbatore (Tamil Nadu), India.
Manuscript received on April 08, 2021. | Revised Manuscript received on June 01, 2021. | Manuscript published on June 30, 2021. | PP: 131-134 | Volume-10 Issue-5, June 2021. | Retrieval Number: 100.1/ijeat.D24360410421 | DOI: 10.35940/ijeat.D2436.0610521
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Danger characterization of tumors from radiology image container to be much precise and quicker with computer aided diagnosis (CAD) implements. Tumor portrayal via such devices can likewise empower non-intrusive prognosis, and foster personalized, and treatment arranging as a piece of accuracy medication. In this study , in cooperation machine learning algorithm strategies to better tumor characterization. Our methodological analysis depends on directed erudition for which we exhibit critical increases with machine learning algorithm, particularly by exploitation a 3D Convolutional Neural Network and Transfer Learning. Disturbed by the radiologists’ understandings of the outputs, we at that point tell the best way to fuse task subordinate feature representations into a CAD framework by means of a diagram regularized inadequate MultiTask Learning (MTL) system with the help of feature fusion.
Keywords: Component; Formatting; Style; Styling; Insert (Key Words)
Scope of the Article: Machine Learning