Improved detection of dry age-related macular degeneration from optical coherence tomography images using adaptive window based feature extraction and weighted ensemble based classification approach.

BACKGROUND: Dry Age-related macular degeneration (AMD), which affects the older population, can lead to blindness when left untreated. Preventing vision loss in elderly needs early identification. Dry-AMD diagnosis is still time-consuming and very subjective, depending on the ophthalmologist. Setting up a thorough eye-screening system to find Dry-AMD is a very difficult task. METHODOLOGY: This study aims to develop a weighted majority voting (WMV) ensemble-based prediction model to diagnose Dry-AMD. The WMV approach combines the predictions from base-classifiers and chooses the class with greatest vote based on assigned weights to each classifier. A novel feature extraction method is used along the retinal pigment epithelium (RPE) layer, with the number of windows calculated for each picture playing an important part in identifying Dry-AMD/normal images using the WMV methodology. Pre-processing using hybrid-median filter followed by scale-invariant feature transform based segmentation of RPE layer and curvature flattening of retina is employed to measure exact thickness of RPE layer. RESULT: The proposed model is trained on 70% of the OCT image database (OCTID) and evaluated on remaining OCTID and SD-OCT Noor dataset. Model has achieved accuracy of 96.15% and 96.94%, respectively. The suggested algorithm's effectiveness in Dry-AMD identification is demonstrated by comparison with alternative approaches. Even though the suggested model is only trained on the OCTID, it has performed well when tested on additional dataset. CONCLUSION: The suggested architecture can be used for quick eye-screening for early identification of Dry-AMD. The recommended method may be applied in real-time since it requires fewer complexity and learning-variables.

Improved detection accuracy of red lesions in retinal fundus images with superlearning approach.

BACKGROUND: Diabetic Retinopathy (DR) is a serious consequence of diabetes that can result to permanent vision loss for a person. Diabetes-related vision impairment can be significantly avoided with timely screening and treatment in its initial phase. The earliest and the most noticeable indications on the surface of the retina are micro-aneurysm and haemorrhage, which appear as dark patches. Therefore, the automatic detection of retinopathy begins with the identification of all these dark lesions. METHOD: In our study, we have developed a clinical knowledge based segmentation built on Early Treatment DR Study (ETDRS). ETDRS is a gold standard for identifying all red lesions using adaptive-thresholding approach followed by different pre-processing steps. The lesions are classified using super-learning approach to improve multi-class detection accuracy. Ensemble based super-learning approach finds optimal weights of base learners by minimizing the cross validated risk-function and it pledges the improved performance compared to base-learners predictions. For multi-class classification, a well informative feature-set based on colour, intensity, shape, size and texture, is developed. In this work, we have handled the data imbalance problem and compared the final accuracy with different synthetic data creation ratios. RESULT: The suggested approach uses publicly available resources to perform quantitative assessments at lesions-level. The overall accuracy of red lesion segregation is 93.5%, which has increased to 97.88% when data imbalance problem is taken care-off. CONCLUSION: The results of our system have achieved competitive performance compared with other modern approaches and handling of data imbalance further increases the performance of it.

An infection of Enterobacter ludwigii affects development and causes age-dependent neurodegeneration in Drosophila melanogaster.

The effects of teeth-blackening bacteria Enterobacter ludwigii on the physiological system were investigated using the model organism Drosophila melanogaster. The bacteria were mixed with the fly food, and its effect was checked on the growth, development and behaviour of Drosophila. Microbes generate reactive oxygen species (ROS) within the haemolymph of the larvae once it enters into the body. The increased amount of ROS was evidenced by the NBT assay and using 2',7'-dichlorofluorescin diacetate dye, which indicates the mitochondrial ROS. The increased amount of ROS resulted in a number of abnormal nuclei within the gut. Besides that larvae walking became sluggish in comparison with wild type although the larvae crawling path did not change much. Flies hatched from the infectious larvae have the posterior scutellar bristle absent from the thorax and abnormal mechanosensory hairs in the eye, and they undergo time-dependent neurodegeneration as evidenced by the geotrophic and phototrophic assays. To decipher the mechanism of neurodegeneration, flies were checked for the presence of four important bioamines: tyramine, cadaverine, putrescine and histamine. Out of these four, histamine was found to be absent in infected flies. Histamine is a key molecule required for the functioning of the photoreceptor as well as mechanoreceptors. The mechanism via which mouth infectious bacteria E. ludwigii can affect the development and cause age-dependent neurodegeneration is explained in this paper.