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: Recent facts and figures published in various studies in the US show that approximately 27,510 new cases of gastric infections are diagnosed. Furthermore, it has also been reported that the mortality rate is quite high in diagnosed cases. The early detection of these infections can save precious human lives. As the manual process of these infections is timeconsuming and expensive, therefore automated Computer-Aided Diagnosis (CAD) systems are required which helps the endoscopy specialists in their clinics. Generally, an automated method of gastric infection detections using Wireless Capsule Endoscopy (WCE) is comprised of the following steps such as contrast preprocessing, feature extraction, segmentation of infected regions, and classification into their relevant categories. These steps consist of various challenges that reduce the detection and recognition accuracy as well as increase the computation time. In this review, authors have focused on the importance of WCE in medical imaging, the role of endoscopy for bleeding-related infections, and the scope of endoscopy. Further, the general steps and highlight the importance of each step has presented. A detailed discussion and future directions have provided in the last.

The work proposes a computer-based diagnosis method (CBDM) to delineate and assess the corpus callosum (CC) segment from the 2-dimensional (2D) brain magnetic resonance images (MRI). The proposed CBDM consists of two parts: (1) preprocessing and (2) postprocessing sections. The preprocessing tools have a multithreshold technique with the chaotic cuckoo search (CCS) algorithm and a preferred threshold procedure. The postprocessing employs a delineation process for extracting the CC section. The proposed CBDM finally extracts the vital CC parameters, such as total brain area (TBA) and CC area (CCA) to classify the considered 2D MRI slices into the control and autism spectrum disorder (ASD) groups. This attempt considers the benchmark brain MRI database which includes ABIDE and MIDAS for the experimental investigation. The results obtained with ABIDE dataset are further confirmed against the fuzzy C -means driven level set (FCM + LS) and multiphase level set (MLS) technique and the proposed CBDM with Shannon entropy along with active contour (SE + AC) presented improved result in comparison to the existing methodologies. Further, the performance of CBDM is confirmed on MIDAS and clinical dataset. The experimental outcomes approve that the proposed CBDM extracts the CC section from the 2D MR brain images that have higher accuracy compared to alternative techniques.

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