Cancer Detec-Lung Cancer Diagnosis Support System: First Insights

Nelson Faria, Sofia Campelos, Vítor Carvalho

2022

Abstract

Lung cancer is the type of cancer that causes most deaths worldwide and as sooner it is discovered as more possibilities there are for the patient to be treated. An accurate histological classification of tumours is essential for lung cancer diagnosis and adequate patient management. Whole-slide images (WSI) generated from tissue samples can be analysed using Deep Learning techniques to assist pathologists. In this study it is given an overview of the lung cancer exploring the different types of implementations undertaken until the present. These methods show a two-step implementation in which the tasks consist primarily of the detection of the tumour and after on the histologic classification of the tumour. To detect the neoplastic cells, the WSI is split in patches, and then a convolutional neural network is applied to identify and generate a heatmap highlighting the tumour regions. In the next step, features are extracted from the neoplasic regions and submitted in a classifier to determine the histologic type of tumour present in each patch. Moreover, in this paper, it is proposed a possible approach based on the literature review to surpass the limitations found in the actual models, and with better performance and accuracy, that could be used as an aid in the pathological diagnosis of the lung cancer.

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Paper Citation


in Harvard Style

Faria N., Campelos S. and Carvalho V. (2022). Cancer Detec-Lung Cancer Diagnosis Support System: First Insights. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, ISBN 978-989-758-552-4, pages 81-88. DOI: 10.5220/0010767800003123


in Bibtex Style

@conference{bioinformatics22,
author={Nelson Faria and Sofia Campelos and Vítor Carvalho},
title={Cancer Detec-Lung Cancer Diagnosis Support System: First Insights},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,},
year={2022},
pages={81-88},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010767800003123},
isbn={978-989-758-552-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,
TI - Cancer Detec-Lung Cancer Diagnosis Support System: First Insights
SN - 978-989-758-552-4
AU - Faria N.
AU - Campelos S.
AU - Carvalho V.
PY - 2022
SP - 81
EP - 88
DO - 10.5220/0010767800003123