Multilayer Weighted Logistic Regressive Deep Convolution Neural Learning Based Feature Extraction For Liver Tumor Detection.
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Abstract
The diagnosing liver disease is crucial for saving lives and managing the condition effectively. The conventional techniques were implemented for liver tumor diagnosis does not provided higher feature extraction accuracy to identify the presence of cancer at an early stage with minimal time complexity while considering a more number of input images. Therefore, a new Multilayer Weighted Logistic Regressive Deep Convolution Neural learning Based Feature Extraction (MWLRDCNL-FE) model is introduced in order to reduce the complexity of feature extraction during the process of liver tumor detection. Weighted Logistic Regression Analysis is a useful tool for feature selection in machine learning where the weight of each feature is determined by the magnitude of its coefficient value. A larger weight value indicates that the feature has a greater impact on the predicted outcome. The designed MWLRDCNL-FE technique includes two main processes. During the first task, Feature Extraction is performed in convolution layer where it identifies the number of features from input MRI liver images. During the second task, feature selection is performed in max-pooling layer using weighted logistic regression concept to discover the most significant features (i.e. morphological, texture, density features) from input MRI liver images with minimal complexity. By using weights to get feature importance in logistic regression proposed MWLRDCNL-FE model improves the features extraction performance of liver tumor detection with lesser time consumption. The experimental evaluation of proposed MWLRDCNL-FE model is performed by considering metrics such as feature extraction accuracy, feature extraction time, false positive rate with respect to different number of input images...
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