scholarly journals Lung Cancer detection and Classification by using Machine Learning & Multinomial Bayesian

2014 ◽  
Vol 9 (1) ◽  
pp. 69-75 ◽  
Author(s):  
Mr. Sandeep A. Dwivedi ◽  
◽  
Mr. R. P. Borse ◽  
Mr. Anil M. Yametkar
2019 ◽  
Vol 8 (2) ◽  
pp. 3499-3505

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).


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

2021 ◽  
Vol 40 (4) ◽  
pp. 6355-6364
Author(s):  
S. Lalitha

Cancer has been one of the most serious health challenges to the human kind for a long period of time. Lung cancer is the most prevalent type of cancer which shows higher death rates. However, lung cancer mortality rates can be tracked by periodic screening. With the advanced medical science, the society has reaped numerous benefits with respect to screening equipments. Computed Tomography (CT) is one of the popular imaging techniques and this work utilizes the CT images for lung cancer detection. An early detection of lung cancer could prolong the lifetime of the patient and is made effortless by the latest screening technology. Additionally, the accuracy of disease detection can be enhanced with the help of the automated systems, which could support the healthcare experts in effective diagnosis. This article presents an automated lung cancer detection system equipped with machine learning algorithm, which can differentiate between the benign, malignant and normal classes of lung cancer. The accuracy of the proposed lung cancer detection method is around 98.7%, which is superior to the compared approaches.


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