A Deep Learning Framework for Detection and Segmentation of Multiple Artefacts in Endoscopic Images

Kirthika N; Dr.B.Sargunam

A Deep Learning Framework for Detection and Segmentation of Multiple Artefacts in Endoscopic Images

dc.contributor.author	Kirthika N
dc.contributor.author	Dr.B.Sargunam
dc.date.accessioned	2025-06-25T09:18:26Z
dc.date.available	2025-06-25T09:18:26Z
dc.date.issued	2023-05
dc.description.abstract	Endoscopy is a standard procedure for disease surveillance, monitoring inflammations, detect cancer and tumor. During the procedure the organs are visualized. Artefacts, an artificial effect is found to be present in the resultant images. They play a dominant role in increasing procedure time by more than an hour. Hence an efficient algorithm to detect, segment and restore could assist clinician. The artefacts present in an endoscopic image include saturation, specular reflections, blur, bubbles, contrast, blood, instruments and miscellaneous artefacts. The presence of these artefacts acts as a barrier when investigating the underlying tissue for identifying clinical abnormalities. It also affect post processing steps where most of the images captured are discarded due to the presence of artefacts which in turn affects information storage and extracting useful frame for report generation. Endoscopic artefact detection dataset is the only available public dataset holding endoscopic images with annotations for multiple artefacts. Hence, a custom dataset is annotated using the same annotation protocol of endoscopic artefact detection dataset to maintain homogeneity. The algorithms are trained and tested with images from both public and custom dataset for artefact detection. State of the art object detection algorithms such as YOLOv3, YOLOv4 and faster R-CNN are used for detecting artefacts in endoscopic images. The detection algorithm focusses on three important performance parameters namely mean average precision, intersection over union and inference time. The ensemble model outperformed well across all the performance parameters compared with literature. The inference time is reduced by 8.63%, whereas the mAP and IoU are increased by 61.67% and 63.47% respectively. newlineSegmentation algorithms like U-Net with EfficientNetB3 backbone, Link-Net with EfficientNetB3 backbone and U-Net with SE-ResNeXt101 backbone are used to segment the artefacts. The results are assessed with performance parameters like F2 score and Jaccard score.The results proves a phenomenal increase in Jaccard score by 17.36% and F2 score by 17.42% respectively. An image if found to have artefacts after artefact detection,the affected region will be segmented by the proposed segmentation algorithm.To visualize the scope and need of artefact detection and segmentation a simple application is developed to restore the artefacts. The segmented output contains a binary mask using which fast marching algorithm will restore the segmented area. Hence the resulting restored image gives the clinician a better view of the organ. A simple CNN based classifier is proposed to classify polyp. It is found that the classifier's performance is improved by 3.09% when the artefacts in the images are restored. Thus such outcomes when implemented in real time could effectively have a control over the false diagnosis rate, which is the rate at which the disease is misclassified, procedure time and clinician's fatigue as well.
dc.identifier.uri	https://ir.avinuty.ac.in/handle/123456789/17577
dc.language.iso	en
dc.publisher	Avinashilingam
dc.subject	Electronics and Communication Engineering	en
dc.title	A Deep Learning Framework for Detection and Segmentation of Multiple Artefacts in Endoscopic Images
dc.type	Learning Object