Advanced Optimized Counter based Hierarchal Model to Predict Cancer’s Disease from Cancer Patients Neurological Features

Cancer disease prediction based on neurological characteristics of cancer patients is gaining a significant research attention in recent times. The role of data in the processing and analysis of neurological features is critical, and the main goal is to efficiently extract neurological features from cancer patients' data. Random extraction of neurological features from cancer patient data is a new research initiative. Convolutional Neural Networks (CNN) is a promising approach in various healthcare applications to efficiently perform the data processing tasks. Some CNN-based approaches have been proposed to perform efficient cancer disease prediction using remotely sensed neurological features. Cancer disease extraction based on MPDCNN is one of the best CNN approaches used for extracting features and perform disease prediction from Geo-Fan-2 (GF-2) sensing cancer patient data. However, due to its sparse arrangement of optimal boundary, exact neurological features and high amount of training time, it is insufficient to investigate and automate the neurological feature extraction process from the cancer patient's data. A Novel Optimized Multi Feature Contour based Hierarchical Neural Network (NOMFCHNN) is proposed to improve the automatic neurological feature prediction process. NOMFCHNN is made up of expanding neural network features and layers related to inception, which contains the data about network localization, and this approach uses optimal and exact neurological feature matching with extended feature extraction. This method also employs contour map optimization to identify contours based on globalization of cancer patient data along with the output of the identified contour being transmitted to the next identified contour in the selected hierarchical region. Furthermore, the proposed approach evaluates the low- resolution term in cancer patient's data to gain knowledge from the cancer patient's data by obtaining the prediction results of neighbouring optimal and exact neurological features to eliminate small changes or errors. A multi scale feature Prediction module is used to eliminate feature inconsistency between the encoding and decoding phases of the prediction process in order to identify better contours of neurological features from remote sensing cancer patient's data. Extensive experiments on combined repository cancer patient data show that the proposed methodology improves the prediction accuracy and other parameters when compared to the other state-of-the-art methods used to remotely analyze the neurological features.