Colonoscopy is a gold standard procedure for tracking the lower gastrointestinal region. A colorectal polyp is one such condition that is detected through colonoscopy. Even though technical advancements have improved the early detection of colorectal polyps, there is still a high percentage of misses due to various factors. Polyp segmentation can play a significant role in the detection of polyps at the early stage and can thus help reduce the severity of the disease. In this work, the authors implemented several image pre-processing techniques such as coherence transport and contrast limited adaptive histogram equalization (CLAHE) to handle different challenges in colonoscopy images. The processed image was then segmented into a polyp and normal pixel using a U-Net-based deep learning segmentation model named UPolySeg. The main framework of UPolySeg has an encoder–decoder section with feature concatenation in the same layer as the encoder–decoder along with the use of dilated convolution. The model was experimentally verified using the publicly available Kvasir-SEG dataset, which gives a global accuracy of 96.77%, a dice coefficient of 96.86%, an IoU of 87.91%, a recall of 95.57%, and a precision of 92.29%. The new framework for the polyp segmentation implementing UPolySeg improved the performance by 1.93% compared with prior work.

In 1997, Long Short-Term Memory (LSTM) networks were proposed, which significantly changed the landscape of sequential data analysis by resolving the critical issue of the vanishing gradient problem in recurrent neural networks (RNNs). Over the last 25 years, LSTM has advanced from its inception as an innovative solution to its widespread adoption as an essential tool in various fields, including natural language processing (NLP), speech recognition, financial prediction, and healthcare analytics. The present study is a bibliometric review of the evolution of LSTMs. The evolution of LSTM is discussed in terms of its theoretical advancements, architectural developments, and its applications. The study is based on data obtained from the Scopus database, which is then analyzed to identify publication patterns, prominent authors, prominent institutions, and global contributions to the field. The present study is an insightful review of the evolution of LSTM, highlighting its developments and advancements, as well as its applications, to identify its future scope.