Open AccessNon Small Cell Lung Cancer Classification from Histopathological Images using Feature Fusion and Deep CNN
Author(s) -
Nuralam Nuralam,
Md. Mahbubur Rahman,
Khandaker Mohammad Mohi Uddin,
Al Bashir,
Jahanara Akhtar
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e9266.069520
Subject(s) - lung cancer , convolutional neural network , artificial intelligence , pattern recognition (psychology) , adenocarcinoma , computer science , contourlet , feature extraction , redundancy (engineering) , cancer , pathology , medicine , wavelet transform , wavelet , operating system
Lung cancer is the overgrowth of cells in digestive organs. Identifying different types of lung cancer (squamous cell cancer, large cell carcinoma and adenocarcinoma) from lung histopathological images is outrageous works that shorten the chance of infected with lung cancer in the future. This research propounds an accurate diagnosis scheme using various neural network features and fusion of contourlet transform from lung histopathological image. This lesson has used several pre-train models (Alexnet, ResNet50, and VGG-16) in addition to divers scratch models while the pre-train Resnet50 model works better. The two reduction techniques (Principle Component Analysis (PCA) and Minimum Redundancy Maximum Relevance (MRMR)) have used to classify the type of lung cancer with the extraction of the most significant properties. In Convolution Neural Network (CNN) based lung cancer detection, the reduction approach PCA performs better. This proposed methodology is performed on ordinary datasets and establishes comparative better performance. The accuracy of this paper is 98.5%, sensitivity 96.50, specificity 97.00%, which is more effective than other approaches.