End to End System for Pneumonia and Lung Cancer Detection using Deep Learning
Venkata Tulasiramu Ponnada1, S.V. Naga Srinivasu2
1DVenkata Tulasiramu Ponnada, Research Scholar, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, Andhra Pradesh, India.
2Dr. S. V.Naga Srinivasu, Professor, Computer science and engineering, Narasaraopeta Engineering College, Narasaraopet, Andhra Pradesh, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2888-2893 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8791088619/2019©BEIESP | DOI: 10.35940/ijeat.F8791.088619
Open Access | Ethics and Policies | Cite | Mendeley
© 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 Deep learning solutions for medical image analysis are offered a promising alternative solution to self-learning problem-specific features and gave a new facet for computer vision challenges. The early detection of pneumonia and lung cancer plays big role in saving the life. Any method or system contributing to early disease detection is likely to reduce the dearth rate of diseases. Our previous work  proposed an efficient CNN (EFFI-CNN) for Lung cancer detection. This paper presents a system to detect the pneumonia and lung cancer using deep leaning techniques (ESPLDUDL). The system leverages the EFFI-CNN, Raspberry Pi and Tensor processing Unit (TPU). The system configuration raises the bar in detection results and technology front.
Keywords: Pneumonia detection, Lung cancer detection, deep learning, Machine learning, Neural networks, CN, Raspberry Pi, TPU