Lung Cancer Detection and Classification based on Image Processing and Statistical Learning

TL;DR

This paper presents a new image processing and statistical learning approach for early lung cancer detection using CT scans, aiming to improve accuracy over existing methods.

Contribution

It introduces a novel algorithm that combines image processing and statistical learning for lung cancer classification, tested on Kaggle CT datasets.

Findings

Achieved 72.2% accuracy on Kaggle lung CT dataset

Demonstrated improved detection performance over existing systems

Validated method on 198 CT slices from various cancer stages

Abstract

Lung cancer is one of the death threatening diseases among human beings. Early and accurate detection of lung cancer can increase the survival rate from lung cancer. Computed Tomography (CT) images are commonly used for detecting the lung cancer.Using a data set of thousands of high-resolution lung scans collected from Kaggle competition [1], we will develop algorithms that accurately determine in the lungs are cancerous or not. The proposed system promises better result than the existing systems, which would be beneficial for the radiologist for the accurate and early detection of cancer. The method has been tested on 198 slices of CT images of various stages of cancer obtained from Kaggle dataset[1] and is found satisfactory results. The accuracy of the proposed method in this dataset is 72.2%