Detection of lung nodule and cancer using novel Mask-3 FCM and TWEDLNN algorithms

Lung Cancer (LC) is reported as common cause of death all over the world. The detection of cancer can save many lives and help likelihood of survival. Physicians use CT (computed tomography) for examination of the cancer in lung with the help of computer-aided diagnosis (CAD) for efficient detection and diagnosis. The CAD uses different machine learning techniques and signal processing approaches but processing time and accuracy of CAD remains challenging issues. An efficient Deep Learning (DL) methodology is proposed for lung cancer detection utilizing Target based Weighted Elman DL Neural Network (TWEDLNN), and Mask Unit (MU) based 3FCM algorithm. The proposed work includes lung image segmentation using Geometric mean-based Otsu Thresholding (GOT); contrast enhancement (CE) using Modified Clip limit-based Contrasts Limited Adaptive Histograms Equalization (MC-CLAHE); Feature Extraction (FE); Classification of Features using TWEDLNN; and MU based FCM algorithm for LN (lung nodule) detection. We have used CT images of LIDC-IDRI database for the implementation. We have compared the proposed work with existing techniques to confirm that the TWEDLNN detects LC more efficiently and the accuracy of proposed work is also improved as 96%. The performance of proposed MC-CLAHE is authenticated by contrasting the proposed technique's performance with prevailing techniques, CLAHE, Gaussian, Median, and Wiener filters. The proposed method has resulted PSNR of 24.2573 and MSE value of 292.98, which are better than all existing Techniques.

Automated summary

A methodology for lung cancer detection utilizing deep learning neural networks and algorithms is proposed in this study, achieving significant results in lung image segmentation, contrast enhancement, and feature extraction, with an efficiency of up to 96%. By comparing the new method with existing techniques, its superiority in image processing is validated.