Automated lung cancer diagnosis using swarm intelligence with deep learning

ABSTRACT

In worldwide, lung cancer is amajor threatening issue for humans that increase the mortality rate. Here, the existing techniques are suffered from huge false-positiverates. Such kind of limitations encourages the researchers to designa novel automated lung cancer identification model for providing highly accurate outcome in the early phase. Thus, the major goal ofthis research work is to design and initiate the lung cancer identification framework using an enhanced deep learning model. The standard dataset attained for the analysis is offered to pre-processing part. Then, the Frangi filter is utilized for removingthe vessel from the image. The adoption of modified Fuzzy C-Means Clustering (FCM) with local thresholding is employed for the nodule segmentation, where parameter tuning is executed by Modified Dimension Range-based Cat Swarm Optimization (MDR-CSO). The resultant texture and shape features are subjected to the Enhanced Recurrent Neural Network (ERNN) for diagnosing lung cancer. Throughout theresult analysis, the accuracy and MCC rate of the developed model is95% and 91%. Thus, the result analysis of the offered method providesa better lung cancer detection rate than the classical techniquesthroughout the experimental analysis.

KEYWORDS:

• Lung cancer diagnosis
• enhanced recurrent neural network
• modified dimension range-based cat swarm optimisation
• Frangi filter
• modified fuzzy C-means clustering

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.