A frame work for the detection and Diagnosis of Lung Tumors using Deep learning Methods

P.Jagadeesh , Et Al

doi:10.17762/turcomat.v12i2.2051

Abstract

The detection of tumor pixels in lung images is complex task due to its low contrast property. Hence, this paper uses deep learning architectures for both the detection and diagnosis of lung tumors in Computer Tomography (CT) images. In this article, the tumors are detected in lung CT images using Convolutional Neural Networks (CNN) architecture with the help of data augmentation methods. This proposed CNN architecture classifies the lung images into two categories as tumor images and normal images. Then, the segmentation method is used to segment the tumor pixels in the lung CT images and the segmented tumor regions are classified into either mild or severe using proposed CNN architecture.

Highlights

The tumor cells are developed in the lung regions due to the genetic reasons and improper food consumption.More alcohol intake is the main reason for the formation of abnormalities in lung regions of the human body [1]
These limitations are resolved by implementing the deep learning algorithm for the detection and classifications of lung tumors in lung Computer Tomography (CT) images
The segmentation method is used to segment the tumor pixels in the lung CT images and the segmented tumor regions are classified into either mild or severe using proposed Convolutional Neural Networks (CNN) architecture

Summary

Introduction

The tumor cells are developed in the lung regions due to the genetic reasons and improper food consumption. The feature computation is more complex in machine learning algorithms and it requires large number of source lung CT images for improving the classification or tumor detection accuracy. Lakshmanaprabu et al (2019) utilized the optimal deep learning algorithm for the detection and classification of tumor affected lung CT images This method was entirely based on the optimal features which were computed from the source grained lung CT images. These optimal features were data augmented and classified using developed CNN architecture. The authors integrated the proposed non-linear probabilistic algorithm with the conventional deep learning architecture to find the tumor pixels in lung CT images. The authors obtained 91% of average tumor diagnosis index, 93.6% of average segmentation accuracy and 95.9% of average sensitivity using their proposed works

Proposed Methodologies

Results and Discussions

Conclusions