The human gastrointestinal (GI) tract is sus-ceptible to a myriad of diseases that can profoundly impact health. Therefore, timely detection and intervention are critical in halting the progression of these diseases and preventing their potential transformation into cancer. Regular screenings, diagnostic tests, and early symptom recognition play vital roles in ensuring early intervention and better patient outcomes in managing GI-related conditions. In recent years, scientists have increasingly turned to advanced technologies, particularly deep learning algorithms, to revolutionize the detection and segmentation of colorectal anomalies. Leveraging the power of artificial intelligence, researchers are exploring sophisticated deep learning models capable of analyzing vast amounts of endoscopic imagery with remarkable precision and efficiency. In our proposed paper, we introduce an approach to the automated segmentation of colorectal anomalies, through the development of an end-to-end architecture named VGGI6U-Net. The architecture enhances the capability of the framework for precise segmentation By leveraging the features of VGG 16 and integrating an attention mechanism into the U-Net framework. The model exhibits promising performance in accurately identifying and delineating polyps and bleeding regions within images. The incorporation of the attention mechanism enables the network to focus on salient features, thereby further improving segmentation accuracy and reducing false positives.

Globally, colorectal cancer is the primary cause of cancer-related death. Colonoscopy is currently one of the most common ways to identify precancerous gastrointestinal disorders. Thus, early and precise polyp segmentation is of therapeutic importance in reducing the risk of developing cancer. The manual examination is a tedious and time-consuming procedure for physicians. Many computer algorithms have been created by scientists to automatically identify problems from colonoscopic images. In order to further increase polyp segmentation performance, we provide in this study an attDenseU-Net design that concurrently includes the attention mechanism and U-Net. AttDenseU-Net reduces the amount of background in an input image while emphasizing key components by inserting a sequence of attention units in between relevant downs amp ling and upsampling operations. Integrating DenseNet blocks into U-Net architecture helps to more effective and efficient feature learning, and improved gradient flow. A publicly available dataset called K vasir-SEG was utilized in this study to evaluate and confirm the proposed approach. Our model's accuracy rate is 86.31 %, its Dice coefficient is 91.48%, and its Jaccard index is 84.30 %. The experiment findings show that the proposed AttDenseU-Net outperforms its baselines and provide s performance on par with existing polyp segmentation methods.

Colorectal cancer is the main reason for mortality from cancer globally. One of the most popular methods for spotting precancerous gut illnesses right now is a colonoscopy. As a result, early and accurate polyp segmentation has great therapeutic significance in lowering the likelihood that cancer may develop. For doctors, the manual examination is a laborious and time-consuming process. In order to automatically separate the abnormalities from endoscopic pictures, numerous computer algorithms have been developed by scientists. In this research, we introduce an attention U-Net architecture that simultaneously incorporates the attention mechanism and U-Net for further performance improvement of polyp segmentation. AttResU-Net inserts a series of attention units between related downsampling and upsampling processes in order to minimize outside regions in an input image while accentuating important elements. This paper used CVC-ClinicDB, a publicly accessible dataset to assess and validate the suggested strategy. Our model has a 97.46% accuracy rate, a 90.85% Dice coefficient, and a 83.26% Jaccard index. The results of the trial demonstrate that the suggested AttentionU-Net surpasses its baselines and offers performance comparable to current polyp segmentation techniques.

Wireless capsule endoscopy (WCE) is a technology that captures images of the digestive tract with a pill-sized camera. Capsule endoscopies are used to exclude or diagnose disorders such as bleeding, early symptoms of gastrointestinal cancer, abdominal pain, Crohn's disease, Celiac disease, polyps, and ulcers. However, the main cause for a capsule endoscopy is to scout for small intestine haemorrhage. Because of the technological limits, the images are low quality and feature multiple orientations due to the capsule's free mobility. In this study, we propose a technique for detecting bleeding in WCE images. We deploy a deep neural network that uses the Inception-ResNet-V2 model for its high level, combined with a low-level model that is a convolutional neural network (CNN), to attain better classification performance. The proposed methods' average accuracy is 98.5 %, with sensitivity, specificity, and precision of 98.5 %, 99 %, and 98.5 %, respectively. It clearly shows that our method outperforms state-of-the-art approaches in detecting haemorrhage.

The human gastrointestinal (GI) tract may be infected by various diseases. If not detected at early stages, these abnormalities have the possibility to progress into gastric cancer, which is a common type of malignancies with yearly global cases exceeding one million. Endoscopy is a routinely used strategy for the examination of gastrointestinal tract diseases. During the examination, and due to many reasons like irregular morphologies, a huge number of frames, and exhaustion, gastrologists can miss some abnormalities. Thus, the automated classification of anomalies in endoscopic images is becoming necessary to assist medical diagnosis and reduce the cost and time of the medical process. Recent advances and high performance of deep learning techniques make it the best choice to adopt as a computer-aided-diagnosis strategy. In this paper, a novel deep learning model based deep convolutional neural network is proposed. Our model aims to automatically detect diseases from endoscopic images. The newly designed architecture is validated on the publicly available dataset KVASIR, which contains 8000 images. The results of our CNN approach compared to other well known pre-trained models showed important improvement and achieved 96.89% in terms of accuracy. The experiments demonstrated that the system can perform a high detection level without any human intervention.