A fully automated framework for lung tumour detection, segmentation and analysis

TL;DR

This paper presents a fully automated framework for lung tumour detection, segmentation, and analysis in CT scans, significantly reducing manual effort and improving diagnostic accuracy with 98.14% precision.

Contribution

It introduces a novel automated system combining image processing, segmentation, and ensemble classification for lung tumour analysis from CT scans.

Findings

Achieved 98.14% accuracy in tumour detection.

Automated analysis reduces manual workload.

Provides comprehensive tumour metrics for clinical use.

Abstract

Early and correct diagnosis is a very important aspect of cancer treatment. Detection of tumour in Computed Tomography scan is a tedious and tricky task which requires expert knowledge and a lot of human working hours. As small human error is present in any work he does, it is possible that a CT scan could be misdiagnosed causing the patient to become terminal. This paper introduces a novel fully automated framework which helps to detect and segment tumour, if present in a lung CT scan series. It also provides useful analysis of the detected tumour such as its approximate volume, centre location and more. The framework provides a single click solution which analyses all CT images of a single patient series in one go. It helps to reduce the work of manually going through each CT slice and provides quicker and more accurate tumour diagnosis. It makes use of customized image processing and image segmentation methods, to detect and segment the prospective tumour region from the CT scan. It then uses a trained ensemble classifier to correctly classify the segmented region as being tumour or not. Tumour analysis further computed can then be used to determine malignity of the tumour. With an accuracy of 98.14%, the implemented framework can be used in various practical scenarios, capable of eliminating need of any expert pathologist intervention.