Efficient CNN for Lung Cancer Detection
Venkata Tulasiramu Ponnada1, S.V. Naga Srinivasu2 

1Venkata Tulasiramu Ponnada, Research Scholar, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, Andhra Pradesh 522510, India.
2Dr. S. V. Naga Srinivasu, Professor, Computer Science and engineering, Narasaraopeta Engineering College, Narasaraopet, Andhra Pradesh 522601, India.

Manuscript received on 18 March 2019 | Revised Manuscript received on 23 March 2019 | Manuscript published on 30 July 2019 | PP: 3499-3503 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2921078219/19©BEIESP | DOI: 10.35940/ijrte.B2921.078219
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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 machine learning based solutions for medical image analysis are successful in detection of wide variety of anomalies in imaging procedures. The aim of the medical image analysis systems based on machine learning methods is to improve the accuracy and minimize the detection time. The aim in turn contributes to early disease detection and extending the patient life. This paper presents an efficient CNN (EFFI-CNN) for Lung cancer detection. EFFI-CNN consists of seven CNN layers (i.e. Convolution layer, Max-Pool layer, Convolution layer, Max-Pool layer, fully connected layer, fully connected layer and Soft-Max layer). EFFI-CNN uses lung CT scan images from LIDC-IDRI and Mendeley data sets. EFFI-CNN has a unique combination of CNN layers with parameters (Depth, Height, Width, filter Height and filter width).
Index Terms: Lung Cancer Detection, Machine Learning, Edge AI System, CNN, Deep Learning and Neural Networks.

Scope of the Article: Machine learning