Diagnosis

This atlas and textbook aims to provide diverse pictures, facilitating the readers to learn fundus diseases. Retinal detachment is a common group of vitreo-retinal diseases that causes blindness. The diagnosis and differential diagnosis are dependent mostly on ophthalmolscopy and fundus imaging technology. With the development of imaging technology, it is getting easier for ophthalmologist to make quick and accurate decision on the diagnosis.

This atlas and textbook aims to provide diverse pictures, facilitating the readers to learn fundus diseases. Retinal detachment is a common group of vitreo-retinal diseases that causes blindness. The diagnosis and differential diagnosis are dependent mostly on ophthalmolscopy and fundus imaging technology. With the development of imaging technology, it is getting easier for ophthalmologist to make quick and accurate decision on the diagnosis.

Presenting the latest developments and research results on fault diagnosis approaches using computational intelligence methodologies, this book opens with a review of the state-of-the-art before focusing on various theoretical aspects of computational intelligence methodologies applied to real-world fault diagnosis problems.

This book offers a selection of papers in the field of fault detection and diagnosis, promoting new research results in the field, which come to join other publications in the literature.

This book covers all the aspects related to the laboratory diagnosis of Mycobacterium. Laboratory diagnosis includes microscopic to advance molecular diagnosis. Moreover, it deals with the methods used to detect antimycobacterial susceptibility tests and drug resistance.

This book provides a state-of-the-art guide to Machine Learning (ML)-based techniques that have been shown to be highly efficient for diagnosis of failures in electronic circuits and systems. The methods discussed can be used for volume diagnosis after manufacturing or for diagnosis of customer returns. Readers will be enabled to deal with huge amount of insightful test data that cannot be exploited otherwise in an efficient, timely manner. After some background on fault diagnosis and machine learning, the authors explain and apply optimized techniques from the ML domain to solve the fault diagnosis problem in the realm of electronic system design and manufacturing. These techniques can be used for failure isolation in logic or analog circuits, board-level fault diagnosis, or even wafer-level failure cluster identification. Evaluation metrics as well as industrial case studies are used to emphasize the usefulness and benefits of using ML-based diagnosis techniques.