A Deep Learning Mechanism for Medical Image Investigation using Convolutional Autoencoder Neural Network
Nitin Tyagi1, Sandhya Tarar2, Sandeep Gupta3

1Nitin Tyagi, Research scholar, Department of Computer Science and Engineering, Shri Venketeshwara University, Gajraulla, India.

2Sandhya Tarar, Department of Computer Science, School of Information and Communications Technology, Gautam Buddha University, Greater Noida, India.

3Sandeep Gupta, Department of Computer Science, Jagan Institute of Management Studies, Engineering Management Technical Campus, Greater Noida, India.

Manuscript received on 03 April 2019 | Revised Manuscript received on 10 April 2019 | Manuscript Published on 13 April 2019 | PP: 97-102 | Volume-8 Issue-6C April 2019 | Retrieval Number: F12250486C19/19©BEIESP

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Abstract: In today’s scenario, computer tomography (CT) is broadly used to help illness determination. Particularly, Computer aided diagnosis (CAD) in light of Artificial Intelligence (AI) as of late shows its significance in clever medicinal services. In any case, it is an extraordinary test to set up a satisfactory marked dataset for CT investigation help, because of the protection what’s more, collateral affair. Subsequently, this paper presents a convolutional autoencoder (CAE) deep learning structure to help unsupervised picture highlights learning for lung knob via unlabeled data, which just needs a little measure of named information for proficient element learning. By complete analysis, it demonstrates that the plot proposed here is better than different methodologies, which viably takes care of the characteristic work serious issue amid counterfeit picture marking. In addition, it checks that the proposed work approach can be stretched out for similitude estimation of lung knobs pictures. Particularly, the highlights separated through unsupervised learning (USL) are too material in other related situations.

Keywords: Autoencoder (AE); Convolutional Autoencoder Neural Network (CANN); Convolutional Neural Network (CNN); Deep Learning;, Highlight Learning; Unsupervised Learning (USL)
Scope of the Article: Computer Science and Its Applications