Early detection and proper screening are essential to prevent vision loss due to glaucoma. In recent years, convolutional neural network (CNN) has been successfully applied to the color fundus images for the automatic detection of glaucoma. Compared to the existing automatic screening methods, CNNs have the ability to extract the distinct features directly from the fundus images. In this paper, a deep learning architecture based on a CNN is designed for the classification of glaucomatous and normal fundus images. An 18–layer CNN is designed and trained to extract the discriminative features from the fundus image. It comprises of four convolutional layers, two max pooling layers, and one fully connected layer. A two–stage tuning approach is proposed for the selection of suitable batch size and initial learning rate. The proposed network is tested on DRISHTI–GS1, ORIGA, RIM–ONE2 (release 2), ACRIMA, and large–scale attention–based glaucoma (LAG) databases. Rotation data augmentation technique is employed to enlarge the dataset. Randomly selected 70% of images are used for training the model and remaining 30% images are used for testing. An overall accuracy of 86.62%, 85.97%, 78.32%, 94.43%, and 96.64% are obtained on DRISHTI–GS1, RIM–ONE2, ORIGA, LAG, and ACRIMA databases, respectively. The proposed method has achieved an accuracy, sensitivity, specificity, and precision of 96.64%, 96.07%, 97.39%, and 97.74%, respectively, for ACRIMA database. Compared to other existing architectures, the proposed method is robust to Gaussian noise and salt–and–pepper noise.

Exposure of active pharmaceutical compounds (APCs) to the environment during human use is of potential importance in the emergence of drug resistance, changing soil microbiota and their residual effect on living organisms. Thus, this study aimed to assess the extent of exposure of APCs in the hydrologic cycle in and around New Delhi. This study analyzed the presence of 28 drugs from different classes in the surface water (river Yamuna) and aquifers collected from 48 places in Delhi (within the radius of 40 km). The collected water samples were quantified for APCs content using LC-MS/MS. This study revealed that aquifers are extensively affected in most areas based on the accumulation of APCs in water resources to the levels > 0.01 μg/L. Interestingly, a geographical plot of total APCs studied indicated clustering in aquifers with such high levels closer to an unscientific landfill. This 30-year-old un-segregated landfill is found to drain leachate into surface water that had high APCs. This study further revealed that apart from therapeutic usage, the main source of ecological exposure could be due to the disposal of unused and expired pharmaceutical compounds into landfills. For the first time, this study revealed the existence of antimicrobial agents and other APCs in the aquifers of Delhi with levels > 0.1 μg/L, which is a matter of serious concern in terms of multi-drug resistance and other environmental perils. This study warrants the enforcement of regulations for the disposal of unused/expired APCs in high-density population areas.

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Glaucoma is a generic name for a group of diseases which causes progressive optic neuropathy and vision loss due to degeneration of the optic nerves. Optic nerve cells act as transducer and convert light signal entered into the eye to electrical signal for visual processing in the brain. The main risk factors of glaucoma are elevated intraocular pressure exerted by aqueous humour, family history of glaucoma (hereditary) and diabetes. It causes damages to the eye, whether intraocular pressure is high, normal or below normal. It causes the peripheral vision loss. There are different types of glaucoma. Some glaucoma occurs suddenly. So, detection of glaucoma is essential for minimizing the vision loss. Increased cup area to disc area ratio is the significant change during glaucoma. Diagnosis of glaucoma is based on measurement of intraocular pressure by tonometry, visual field examination by perimetry and measurement of cup area to disc area ratio from the color fundus images. In this paper the different signal processing techniques are discussed for detection and classification of glaucoma.