EXPLORING MACHINE LEARNING TECHNIQUES IN LUNG CANCER DETECTION VIA ABSOLUTE INTEGRAL-BASED ANALYSIS

Abstract

This paper aims to provide accurate lung cancer detection using a machine-learning technique has been performed. Here, the proposed machine learning technique, Absolute Integral Based Analysis has been implemented to detect accurate cancer affected parts in the lungs. Matlab coding has been used to perform AIA for the detection of cancer-affected parts or cells in the lungs. This paper has taken a dataset from the appropriate link where research data are available. Performance parameters like accuracy, sensitivity, specificity, precision, recall, f1 score, and gmean and validation accuracy are evaluated accordingly. The Above proposed method Absolute Integral Based Analysis (AIA) machine learning technique detects accurate cancer affected parts in the lungs efficiently and it provides improved accuracy and gmean.

Keywords: DIP (digital image processing), ML (machine learning), AIA (Absolute Integral based Analysis), AI (Artificial Intelligence), CAD (Computer Aided Diagnosis).