Lung cancer survival rate is very limited post-surgery irrespective it is “small cell and non-small cell”. Lot of work have been carried out by employing machine learning in life expectancy prediction post thoracic surgery for patients with lung cancer. Many machine learning models like Multi-layer perceptron (MLP), SVM, Naïve Bayes, Decision Tree, Random forest, Logistic regression been applied for post thoracic surgery life expectancy prediction based on data sets from UCI. Also, work has been carried out towards attribute ranking and selection in performing better in improving prediction accuracy with machine learning algorithms So accordingly, we here have developed Deep Neural Network based approach in prediction of post thoracic Life expectancy which is the most advanced form of Neural Networks . This is based on dataset obtained from Wroclaw Thoracic Surgery Centre machine learning repository which contained 470 instances On comparing the accuracy, the results indicate that the deep neural network can be efficiently used for predicting the life expectancy.

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Radially symmetric wavelets possessing multiresolution framework are found to be useful in different fields like Pattern recognition, Computed Tomography (CT) etc. The compactly supported wavelets are known to be useful for localized operations in applications such as reconstruction, enhancement etc. In this work we introduce a novel way of designing compactly supported radial wavelets in L 2 pR 2 q from a 1D Daubechies wavelets and obtain a reconstruction formula possessing multiresolution framework. Further, we demonstrate the usefulness of our radial wavelets in Tomography.

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