Towards a Connected Mobile Cataract Screening System: A Future Approach.

Advances in computing and AI technology have promoted the development of connected health systems, indirectly influencing approaches to cataract treatment. In addition, thanks to the development of methods for cataract detection and grading using different imaging modalities, ophthalmologists can make diagnoses with significant objectivity. This paper aims to review the development and limitations of published methods for cataract detection and grading using different imaging modalities.

Optimal Training Configurations of a CNN-LSTM-Based Tracker for a Fall Frame Detection System.

In recent years, there has been an immense amount of research into fall event detection. Generally, a fall event is defined as a situation in which a person unintentionally drops down onto a lower surface. It is crucial to detect the occurrence of fall events as early as possible so that any severe fall consequences can be minimized.

The Base Frame of the Algerian E-Health System: Methodology to Select the IHE Integration Profiles and the Standards to Control Interoperability.

The use of standards and Integrating the Healthcare Enterprise (IHE) is a crucial technique for supporting, monitoring and controlling the integration profiles in healthcare organization systems. The risk of "no-interoperability" increases when several standards are used. Lack of experience in developing countries, particularly in Algeria, increases this risk.

Low-contrast X-ray enhancement using a fuzzy gamma reasoning model.

X-ray images play an important role in providing physicians with satisfactory information correlated to fractures and diseases; unfortunately, most of these images suffer from low contrast and poor quality. Thus, enhancement of the image will increase the accuracy of correct information on pathologies for an autonomous diagnosis system. In this paper, a new approach for low-contrast X-ray image enhancement based on brightness adjustment using a fuzzy gamma reasoning model (FGRM) is proposed.

Design and development of a content-based medical image retrieval system for spine vertebrae irregularity.

Background: Content-based medical image retrieval (CBMIR) system enables medical practitioners to perform fast diagnosis through quantitative assessment of the visual information of various modalities.

Methods: In this paper, a more robust CBMIR system that deals with both cervical and lumbar vertebrae irregularity is afforded. It comprises three main phases, namely modelling, indexing and retrieval of the vertebrae image.

Robust identification of polyethylene terephthalate (PET) plastics through Bayesian decision.

Recycling is one of the most efficient methods for environmental friendly waste management. Among municipal wastes, plastics are the most common material that can be easily recycled and polyethylene terephthalate (PET) is one of its major types. PET material is used in consumer goods packaging such as drinking bottles, toiletry containers, food packaging and many more.