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Colorectal Polyp Segmentation: Impact of Combining Different Datasets on Deep Learning Model Performance
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Orientador(es)
Resumo(s)
Colorectal cancer is a major health concern, ranking as one
of the most common and deadly forms of cancer. It typically begins as
polyps, which are abnormal growths in the intestinal mucosa. Identifying
and removing these polyps through colonoscopy is a crucial preventative
measure. However, even experienced professionals can overlook
some polyps during examinations. In this context, segmentation algorithms
can assist medical professionals by identifying areas in an image
that correspond to a polyp. These algorithms, which rely on deep learning,
require extensive image datasets to effectively learn how to identify
and segment polyps. This study aimed to identify public colonoscopy
image datasets that contain polyps and to examine how combining these
datasets might affect the performance of a deep learning-based segmentation
algorithm. After selecting the datasets and defining their combinations,
we trained a segmentation algorithm on each combination.
The evaluation of the trained models showed that merging datasets
can enhance model generalization, with increases of up to 0.242 in the
dice coefficient and 0.256 in the Intersection over Union (IoU). These
improvements could lead to higher diagnostic accuracy in clinical settings,
enhancing efforts to prevent colorectal cancer.
Descrição
Palavras-chave
Deep Learning Colonoscopy Images Dataset Combinations
Contexto Educativo
Citação
Editora
Springer Nature