Lung Cancer Detection from CT Scan Images based on Genetic-Independent Recurrent Deep Learning

TL;DR

This paper presents a novel lung cancer detection model using CT images that combines noise reduction, segmentation with Independent Recurrent Neural Networks, and genetic algorithm optimization to accurately identify nodules.

Contribution

It introduces a new deep learning-based detection approach that integrates genetic algorithms for optimizing segmentation of lung nodules in CT scans.

Findings

Accurately detects lung nodules in CT images

Optimizes segmentation with genetic algorithm

Effective noise reduction and precise nodule localization

Abstract

Lung cancer is one of the prevalence diseases in the world which cause many deaths. Detecting early stages of lung cancer is so necessary. So, modeling and simulating some intelligent medical systems is an essential which can help specialist to accurately determine and diagnose the disease. So this paper contributes a new lung cancer detection model in CT images which use machine learning methods. There are three steps in this model: noise reduction (pre-processing), segmentation (middle-processing) and optimize segmentation for detect exact are of nodules. This article use some filters for noise reduction and then use Independent Recurrent Neural Networks (IndRNN) as deep learning methods for segmentation which optimize and tune by Genetic Algorithm. The results represented that the proposed method can detect exact area of nodules in CT images.