Automatic Diagnosis of Glaucoma from Retinal Images Using Deep Learning Approach.

Glaucoma is characterized by increased intraocular pressure and damage to the optic nerve, which may result in irreversible blindness. The drastic effects of this disease can be avoided if it is detected at an early stage. However, the condition is frequently detected at an advanced stage in the elderly population. Therefore, early-stage detection may save patients from irreversible vision loss. The manual assessment of glaucoma by ophthalmologists includes various skill-oriented, costly, and time-consuming methods. Several techniques are in experimental stages to detect early-stage glaucoma, but a definite diagnostic technique remains elusive. We present an automatic method based on deep learning that can detect early-stage glaucoma with very high accuracy. The detection technique involves the identification of patterns from the retinal images that are often overlooked by clinicians. The proposed approach uses the gray channels of fundus images and applies the data augmentation technique to create a large dataset of versatile fundus images to train the convolutional neural network model. Using the ResNet-50 architecture, the proposed approach achieved excellent results for detecting glaucoma on the G1020, RIM-ONE, ORIGA, and DRISHTI-GS datasets. We obtained a detection accuracy of 98.48%, a sensitivity of 99.30%, a specificity of 96.52%, an AUC of 97%, and an F1-score of 98% by using the proposed model on the G1020 dataset. The proposed model may help clinicians to diagnose early-stage glaucoma with very high accuracy for timely interventions.

Detection of microscopic glaucoma through fundus images using deep transfer learning approach.

Glaucoma disease in humans can lead to blindness if it progresses to the point where it affects the oculus' optic nerve head. It is not easily detected since there are no symptoms, but it can be detected using tonometry, ophthalmoscopy, and perimeter. However, advances in artificial intelligence approaches have permitted machine learning techniques to diagnose at an early stage. Numerous methods have been proposed using Machine Learning to diagnose glaucoma with different data sets and techniques but these are complex methods. Although, medical imaging instruments are used as glaucoma screening methods, fundus imaging specifically is the most used screening technique for glaucoma detection. This study presents a novel DenseNet and DarkNet combination to classify normal and glaucoma affected fundus image. These frameworks have been trained and tested on three data sets of high-resolution fundus (HRF), RIM 1, and ACRIMA. A total of 658 images have been used for healthy eyes and 612 images for glaucoma-affected eyes classification. It has also been observed that the fusion of DenseNet and DarkNet outperforms the two CNN networks and achieved 99.7% accuracy, 98.9% sensitivity, 100% specificity for the HRF database. In contrast, for the RIM1 database, 89.3% accuracy, 93.3% sensitivity, 88.46% specificity has been attained. Moreover, for the ACRIMA database, 99% accuracy, 100% sensitivity, 99% specificity has been achieved. Therefore, the proposed method is robust and efficient with less computational time and complexity compared to the literature available.

Environmental impacts, water footprint and cumulative energy demand of match industry in Pakistan.

A comprehensive life cycle assessment (LCA) was conducted for the matchsticks industry in the Khyber Pakhtunkhwa province of Pakistan to quantify environmental footprint, water footprint, cumulative energy use, and to identify improvement opportunities in the matchsticks manufacturing process. One carton of matchsticks was used as reference unit for this study. Foreground data was collected from the matchsticks industry through questionnaire surveys, personal meetings, and field measurements. The collected data was transformed into potential environmental impacts through the Centre for Environment Studies (CML) 2000 v.2.05 method present by default in the SimaPro v.9.1 software. Water footprint was calculated using methodology developed by Hoekstra et al., 2012 (water scarcity index) V1.02 and cumulative energy demand by SimaPro v.9.1 software. The results showed that transport of primary material (wood logs), sawn wood for matchsticks, red phosphorous, acrylic varnish, and kerosene fuel oil contributed to the overall environmental impacts. Transport of primary materials and sawn timber for matchsticks contributed significantly to abiotic depletion, global warming, eutrophication potential, ozone depletion, corrosion, human toxicity, and aquatic ecotoxicity effects. The total water footprint for manufacturing one carton of matchsticks was 0.265332 m3, whereas the total cumulative energy demand was 715.860 Mega Joules (MJ), mainly sourced from non-renewable fossil fuels (708.979 MJ). Scenario analysis was also conducted for 20% and 30% reduction in the primary material distance covered by trucks and revealed that reducing direct material transport distances could diminish environmental impacts and energy consumption. Therefore, environmental footprint could be minimized through diverting matchsticks industries freight from indigenous routes to high mobility highways and by promoting industrial forestry close to industrial zones in Pakistan. Many industries did not have emissions control systems, exceeding the permissible limit for emissions established by the National Environmental Quality Standards (NEQS) of Pakistan. Thus, installation of emissions control system could also diminish emissions from match industry in Pakistan.