IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques

Abstract

Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automated diagnostic system based on deep learning algorithms is proposed to find polyps. The proposed IRv2-Net model is developed using the UNet architecture with a pre-trained InceptionResNetV2 encoder to extract most features from the input samples. The Test Time Augmentation (TTA) technique, which utilizes the characteristics of the original, horizontal, and vertical flips, is used to gain precise boundary information and multi-scale image features. The performance of numerous state-of-the-art (SOTA) models is compared using several metrics such as accuracy, Dice Similarity Coefficients (DSC), Intersection Over Union (IoU), precision, and recall. The proposed model is tested on the Kvasir-SEG and CVC-ClinicDB datasets, demonstrating superior performance in handling unseen real-time data. It achieves the highest area coverage in the area under the Receiver Operating Characteristic (ROC-AUC) and area under Precision-Recall (AUC-PR) curves. The model exhibits excellent qualitative testing outcomes across different types of polyps, including more oversized, smaller, over-saturated, sessile, or flat polyps, within the same dataset and across different datasets. Our approach can significantly minimize the number of missed rating difficulties. Lastly, a graphical interface is developed for producing the mask in real-time. The findings of this study have potential applications in clinical colonoscopy procedures and can serve based on further research and development.

Automated Colorectal Polyps Detection from Endoscopic Images using MultiResUNet Framework with Attention Guided Segmentation

“…CAD systems have been used by many researchers to develop an automated procedure for identifying polyps. Many researchers trained their models using the Kvasir-SEG dataset, while others worked on post-processing techniques to make the models robust [2,[16][17][18][19][20][21][22][23][24][25][26]. The segmentation of endoscopic images based on semantic information has been extensively studied in medical imaging [27].…”

“…Across the globe, this is a major issue that both affects men and women [1]. The abnormal proliferation of glandular tissue within the colon mucosa is the cause of this type of cancer [2]. These excessive tissue formations, known as polyps, can cause physiological complications.…”

Automated Detection of Colorectal Polyp Utilizing Deep Learning Methods With Explainable AI

“…Additionally, we included Frame Per Second (FPS) as a metric to assess the clinical applicability of the segmentation techniques, considering the inference time during testing. For model loss, Binary Cross-Entropy loss is evaluated simultaneously [57]. The formulas are:…”