scholarly journals Efficient CNN for Lung Cancer Detection

2019 ◽  
Vol 8 (2) ◽  
pp. 3499-3505
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Image Analysis ◽  
Cancer Detection ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Filter Width ◽  
Early Disease Detection ◽  
Fully Connected ◽  
Lung Cancer Detection

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).

scholarly journals Edge AI System for Pneumonia and Lung Cancer Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 1908-1915
Keyword(s):  
Lung Cancer ◽  
Image Analysis ◽  
Cancer Detection ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Input Image ◽  
Machine Learning Algorithms ◽  
Image Capturing ◽  
Decision Supporting ◽  
Lung Cancer Detection

The fabulous success of machine learning algorithms for medical image analysis lead the computer aided disease detection systems for medical diagnosis. This paper presents a system “Edge AI System for Pneumonia and Lung Cancer Detection (EASPLD)". The EASPLD is a unique and one stop solution to detect Pneumonia and Lung cancer. The system is used as a clinician decision supporting system or user system to detect the pneumonia and lung cancer. EASPLD uses deep learning techniques such as convolution neural network (CNN). The proposed solution uses medical image analysis techniques and or methods to develop the system. EASPLD proposed a CNN (EASPLD-CNN) EASPLD-CNN uses seven convolution layers and one max pool layer with 3x3 and 5x5 convolutions, whereas other proposed solutions uses either 3X3 or 5X5 convolutions. In our paper, we used the lung X-Ray and CT scan images from LIDC-IDRI[1] and Mendeley[2]. EASPLD consists of Input image capturing system (IICS), Image enhancement system (IES), EASPLD engine and Results reporting engine (RRE. The EASPLD system output is notified to the end user, i.e. clinician and or a patient in the form of visual, text and email notification.

scholarly journals End to End System for Pneumonia and Lung Cancer Detection using Deep Learning

2019 ◽  
Vol 8 (6) ◽  
pp. 2888-2893
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Medical Image Analysis ◽  
Raspberry Pi ◽  
Processing Unit ◽  
Promising Alternative ◽  
Early Disease Detection ◽  
Self Learning ◽  
Lung Cancer Detection

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 [3] 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

scholarly journals A Survey of Crowdsourcing in Medical Image Analysis

2020 ◽  
Vol 7 ◽  
pp. 1-26 ◽  
Author(s):  
Silas Nyboe Ørting ◽  
Andrew Doyle ◽  
Arno Van Hilten ◽  
Matthias Hirth ◽  
Oana Inel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Image Analysis ◽  
Medical Image ◽  
Large Scale ◽  
Medical Image Analysis ◽  
Machine Learning Algorithms ◽  
Imaging Analysis ◽  
High Quality ◽  
Comprehensive Literature Review

Rapid advances in image processing capabilities have been seen across many domains, fostered by the  application of machine learning algorithms to "big-data". However, within the realm of medical image analysis, advances have been curtailed, in part, due to the limited availability of large-scale, well-annotated datasets. One of the main reasons for this is the high cost often associated with producing large amounts of high-quality meta-data. Recently, there has been growing interest in the application of crowdsourcing for this purpose; a technique that has proven effective for creating large-scale datasets across a range of disciplines, from computer vision to astrophysics. Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis. In this survey, we review studies applying crowdsourcing to the analysis of medical images, published prior to July 2018. We identify common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach. Finally, we discuss future opportunities for development within this emerging domain.

Implementation of Lung Cancer Detection and Recommendation of Oncologist Using Machine Learning

2019 ◽  
Vol 7 (5) ◽  
pp. 467-471
Author(s):  
Nachiket Kelkar ◽  
Niraj Mate ◽  
Atharv Kukade ◽  
Abhijit Kulkarni ◽  
Pradnya Mehta
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Cancer Detection ◽  
Lung Cancer Detection
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