Evaluation of deep neural network architectures in the identification of bone fissures

Automated medical image processing, particularly of radiological images, can reduce the number of diagnostic errors, increase patient care and reduce medical costs. This paper seeks to evaluate the performance of three recent convolutional neural networks in the autonomous identification of fissures over two-dimensional radiological images. These architectures have been proposed as deep neural network types specially designed for image classification, which allows their integration with traditional image processing strategies for automatic analysis of medical images. In particular, we use three convolutional networks: ResNet (residual neural network), DenseNet (dense convolutional network), and NASNet (neural architecture search network) to learn information from a set of 200 images labeled half as fissured bones and half as seamless bones. All three networks are trained and adjusted under the same conditions, and their performance was evaluated with the same metrics. The final results consider not only the model's ability to predict the characteristics of an unknown image but also its internal complexity. The three neural models were optimized to reduce classification errors without producing network over-adjustment. In all three cases, generalization of behavior was observed, and the ability of the models to identify the images with fissures, however the expected performance was only achieved with the NASNet model

Study and Analysis of Fluid Filled Abnormalities in Retina Using OCT Images

Visual impairment is one of the most regularly happening infections in human. The reason being variation from the normal in the different layers of retina because of strange measure of liquid either abundance aggregation or shortage. This paper targets recognizing and assessing the different abnormalities that could be earlier stages to visual deficiency. The proposed target is achieved by means of implementation using Digital Image Processing Technique, starting from preprocessing to classification at various stages. Not restricting to binary classification as normal or abnormal, the proposed system also extends its capacity to classify the input image as Cystoid Macular Edema (CME), Choroidal Neo Vascular Membrane (CNVM), Macular Hole (MH) and normal images. The preprocessing methodology implemented filters to remove the speckle noises which are most common in ultrasound-based imaging system. Random forest classifier was utilized for classifying the input features and also seems to be promising on par with the various existing methodologies

Quality-of-life and clinical outcomes in age-related macular degeneration

Age-related macular degeneration (AMD) is of increasing concern given the ageing population, and the associated economic and social burdens. Vision-related quality-of-life (QoL) is arguably one of the most important factors in the management of those with AMD. Consequently, there is a clear need for an understanding of the clinical outcomes that influence vision-related QoL in order to inform management strategies. The principle aim of the studies described herein was to determine the factors that predict vision-related QoL in those with AMD, over 1 year. Experimental procedures were undertaken at baseline (n=52 individuals with AMD) and repeated after 1 year (n=32 individuals with AMD). These included: visual acuity, contrast sensitivity, reading speed, microperimetry, optical coherence tomography and fundus photography. A questionnaire interview included assessment of vision-related QoL (Impact of Visual Impairment questionnaire), health status (EQ-5D), level of depressive symptoms (PHQ-9) and well-being (Warwick-Edinburgh Well-Being Scale). At baseline, the optimum multiple regression model accounted for 41% of the variance in vision-related QoL and included Mean Total Deviation or Mean Sensitivity with level of depressive symptoms. After 1 year, the optimum model to predict change in vision-related QoL accounted for 43% of the variance and included baseline contrast sensitivity and change in health status and reading speed. The most clinically useful measures of visual function, in identifying those with a reduced QoL or those at risk of a reduced QoL were contrast sensitivity, microperimetry, and reading speed. These outcomes may allow a better understanding of vision-related QoL if they were adopted in a clinical setting. In conclusion, the studies provide sufficient evidence to encourage a review of the clinical outcome measures most relevant to vision-related QoL