Lung cancer detection from CT image using improved profuse clustering and deep learning instantaneously trained neural networks.

• To improve of the quality of lung image and diagnosis of lung cancer. • The lung CT images are collected from CIA dataset. • Noises are eliminated by applying weighted mean histogram equalization approach. • To enhance the quality of the image, the IPCT is used. Automatic lung disease detection is a critical challenging task for researchers because of the noise signals getting included into creative signals amid the image capturing process which may corrupt the cancer image quality thusly bringing about the debased performance. So as to evade this, Lung cancer preprocessing has turned into an imperative stage with the key parts as edge detection, lung image resampling, lung image upgrade and image denoising for improving the nature of input image. Image Denoising is a critical pre-processing task preceding further preparing of the image like feature extraction, segmentation, surface examination, and so forth which elminates the noise whereas retaining the edges and additional complete features to the extent possible. This paper deals with improvement of the quality of lung image and diagnosis of lung cancer by reducing misclassification. The lung CT images are collected from Cancer imaging Archive (CIA) dataset, noise present in the images are eliminated by applying weighted mean histogram equalization approach which successfully removes noise from image, also enhancing the quality of the image, using improved profuse clustering technique (IPCT) for segmenting the affected region. Various spectral features are derived from the affected region. These are examined by applying deep learning instantaneously trained neural network for predicting lung cancer. Eventually, the system is examined by the efficiency of the system using MATLAB based simulation results. The system ensures that 98.42% of accuracy with minimum classification error 0.038. [ABSTRACT FROM AUTHOR]