Non Small Cell Lung Cancer Classification from Histopathological Images using Feature Fusion and Deep CNN

Abstract

<jats:p>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.</jats:p>

A Novel Approach Utilizing Machine Learning for the Early Diagnosis of Alzheimer's Disease

“…In the field of medical science, a number of applications that use various machine learning techniques are presently being used for data analysis and innovation. Machine learning techniques have been used in a number of recent healthcare research studies, including the diagnosis of COVID-19 using X-rays [ 12 , 13 ], the identification of tumors using MRIs [ 14 , 15 ], the prediction of cardiovascular diseases [ 16 , 17 ], dengue [ 18 , 19 ], stroke [ 20 ], and cancer [ 21 , 22 ]. Kader et al [ 23 ] created a model that employed feature selection and extraction strategy to predict Alzheimer's disease using machine learning techniques.…”