ZONING DESIGN FOR HAND­WRITTEN NUMERAL RECOGNITION

Microsoft, Motorola, Siemens, Hitachi, IAPR, NICI, IUF In the field of Optical Character Recognition (OCR), zoning is used to extract topological information from patterns. In this paper zoning is considered as the result of an optimisation problem and a new technique is presented for automatic zoning. More precisely, local analysis of feature distribution based on Shannon's entropy estimation is performed to determine "core" zones of patterns. An iterative region­growing procedure is applied on the "core" zones to determine the final zoning

A PERTURBATION­BASED APPROACH FOR MULTI­CLASSIFIER SYSTEM DESIGN

Microsoft, Motorola, Siemens, Hitachi, IAPR, NICI, IUF This paper presents a perturbation­based approach useful to select the best combination method for a multi­classifier system. The basic idea is to simulate small variations in the performance of the set of classifiers and to evaluate to what extent they influence the performance of the combined classifier. In the experimental phase, the Behavioural Knowledge Space and the Dempster­Shafer combination methods have been considered. The experimental results, carried out in the field of hand­written numeral recognition, demonstrate the effectiveness of the new approach

Bioelectronic technologies and artificial intelligence for medical diagnosis and healthcare

The application of electronic findings to biology and medicine has significantly impacted health and wellbeing. Recent technology advances have allowed the development of new systems that can provide diagnostic information on portable point-of-devices or smartphones. The decreasing size of electronics technologies down to the atomic scale and the advances in system, cell, and molecular biology have the potential to increase the quality and reduce the costs of healthcare. Clinicians have pervasive access to new data from complex sensors; imaging tools; and a multitude of other sources, including personal health e-records and smart environments. Humans are from being able to process this unprecedented volume of available data without advanced tools. Artificial intelligence (AI) can help clinicians to identify patterns from this huge amount of data to inform better choices for patients. In this Special Issue, some original research papers focusing on recent advances have been collected, covering novel theories, innovative methods, and meaningful applications that could potentially lead to significant advances in the field

Semantic segmentation of conjunctiva region for non-invasive anemia detection applications

Technology is changing the future of healthcare, technology-supported non-invasive medical procedures are more preferable in the medical diagnosis. Anemia is one of the widespread diseases affecting the wellbeing of individuals around the world especially childbearing age women and children and addressing this issue with the advanced technology will reduce the prevalence in large numbers. The objective of this work is to perform segmentation of the conjunctiva region for non-invasive anemia detection applications using deep learning. The proposed U-Net Based Conjunctiva Segmentation Model (UNBCSM) uses fine-tuned U-Net architecture for effective semantic segmentation of conjunctiva from the digital eye images captured by consumer-grade cameras in an uncontrolled environment. The ground truth for this supervised learning was given as Pascal masks obtained by manual selection of conjunctiva pixels. Image augmentation and pre-processing was performed to increase the data size and the performance of the model. UNBCSM showed good segmentation results and exhibited a comparable value of Intersection over Union (IoU) score between the ground truth and the segmented mask of 96% and 85.7% for training and validation, respectively