A Survey on Deep Learning Architectures and Frameworks for Cancer Detection in Medical Images Analysis
Thiyagarajan A.1, Murukesh C2
1Thiyagarajan A., Agni College of Technology, Chennai, India.
2Murukesh C., Velammal Engineering College, Chennai, India.
Manuscript received on August 19, 2020. | Revised Manuscript received on August 26, 2020. | Manuscript published on September 10, 2020. | PP: 28-34 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.K76540991120 | DOI: 10.35940/ijitee.K7654.0991120
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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: The various hurdles in machine learning are beaten by deep learning techniques and then the deep learning has gradually become preeminent in artificial intelligence. Deep learning uses neural networks to kindle decisions like humans. Deep learning flourished as an energetic approach and clarity marked its success in various domains. The study includes some dominant deep learning algorithms such as convolution neural network, fully convolutional network, autoencoder, and deep belief network to analyze the medical image and to detect and diagnose of cancer at an early stage. As early as the detection of cancer than to treat the disease is uncomplicated. Early diagnosis was particularly relevant for some cancers such as breast, skin, colon, and rectum, which prohibit the chance to grow and spread. Deep learning contributes to enhanced performance and better prediction in detection of cancer with medical images. The paper presents the study of a few deep learning software frameworks such as tensor flow, theano, caffe, torch, and keras. Tensor Flow provides excellent functionality for deep learning. Keras is a high-level neural network API that operates above on tensor flow or theano. The survey winds up by presenting several future avenues and open challenges that should be addressed by the researcher in the future.
Keywords: Deep learning, Convolutional neural network, Cancer, Framework.