Nelson Faria

Bachelor

Polytechnic Institute of Cávado and Ave

Student

NLST (Learn more about this study)

NLST-818

Jul 22, 2021

Cancer Detec - Lung Cancer Diagnosis Support System

Lung cancer is the type of cancer that causes most deaths worldwide and, the sooner it is discovered, 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 technology to assist pathologists. In the literature review, it is given an overview of the lung cancer, exploring the different types of implementations done until the present. These methods show a two-step implementation in which the tasks consist primarily on 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, the features are extracted from the cancerous regions and submitted in a classifier to determine the histologic type of tumour present in each patch. In this project, should be developed a solution 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 lung cancer.