INVESTIGATING THE ROLE OF ARTIFICIAL INTELLIGENCE IN PANCREATIC CANCER ANALYSIS

Abstract

In a variety of fields, including computer vision, natural language processing, automatic speech recognition, and medical data analysis, deep learning algorithms produce the best results.  Because deep learning algorithms concentrate on layer-wise feature learning and make intelligent judgments on their own, they are distinct from traditional machine learning models.  Based on structural and physicochemical protein features, this study used a deep learning approach to predict the class of shared Gene Alzheimer Parkinson data.  To choose the best feature subset from the CGAP data, the suggested method employs correlation feature selection based on the rank search method. A deep neural network is then used to train the selected feature.  The suggested strategy outperformed all other implemented segmentation techniques when the results were compared with some of the current segmentation methods. Significant health study on cancer forecast is available, and it concerns a variety of body parts and has varying appearances.  Cancer is predicted to be incurable and incapable of being adequately prevented.  Neural networks and machine learning are currently producing promising results for pancreatic picture segmentation.  The chance of accurately identifying cancer is substantially increased by the specific machine learning and image processing algorithms that have been offered for previously used screening systems.  The field of artificial intelligence known as machine learning (ML) links the challenge of drawing conclusions from a collection of samples with common notions.With its vast array of applications, machine learning has emerged as a major challenge in the biomedical field in recent years.  In essence, machine learning (ML) searches an n-dimensional space for the specified collection of data.  On the other hand, image processing has been shown to be quite helpful in identifying the early stages of cancer.  In essence, it attempts to extract some relevant facts from the image by performing specific procedures on it.  Since cancers require a great deal of time and accuracy, machine learning and image processing algorithms provide highly promising results in a very short amount of time.  This serves as a source of inspiration for carrying out this research.