Glaucoma is a chronic eye disease that may lead to permanent vision loss if it is not diagnosed and treated at an early stage. The disease originates from an irregular behavior in the drainage flow of the eye that eventually leads to an increase in intraocular pressure, which in the severe stage of the disease deteriorates the optic nerve head and leads to vision loss. Medical follow-ups to observe the retinal area are needed periodically by ophthalmologists, who require an extensive degree of skill and experience to interpret the results appropriately. To improve on this issue, algorithms based on deep learning techniques have been designed to screen and diagnose glaucoma based on retinal fundus image input and to analyze images of the optic nerve and retinal structures. Therefore, the objective of this paper is to provide a systematic analysis of 52 state-of-the-art relevant studies on the screening and diagnosis of glaucoma, which include a particular dataset used in the development of the algorithms, performance metrics, and modalities employed in each article. Furthermore, this review analyzes and evaluates the used methods and compares their strengths and weaknesses in an organized manner. It also explored a wide range of diagnostic procedures, such as image pre-processing, localization, classification, and segmentation. In conclusion, automated glaucoma diagnosis has shown considerable promise when deep learning algorithms are applied. Such algorithms could increase the accuracy and efficiency of glaucoma diagnosis in a better and faster manner.

Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich–Pyle (GP) or Tanner–Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months², respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively.

Apples are one of the most consumed fruits, and they require efficient harvesting procedures to remains in optimal states for a longer period, especially during transportation. Therefore, automation has been adopted by many orchard operators to help in the harvesting process, which includes apple localization on the trees. The de facto sensor that is currently used for this task is the standard camera, which can capture wide view information of various apple trees from a reasonable distance. Therefore, this paper aims to produce the output mask of the apple locations on the tree automatically by using a deep semantic segmentation network. The network must be robust enough to overcome all challenges of shadow, surrounding illumination, size variations, and occlusion to produce accurate pixel-wise localization of the apples. A high-resolution deep architecture is embedded with an optimized design of group and shuffle operators (GSO) to produce the best apple segmentation network. GSO allows the network to reduce the dependency on a few sets of dominant convolutional filters by forcing each smaller group to contribute effectively to the task of extracting optimal apple features. The experimental results show that the proposed network, GSHR-Net, with two sets of group convolution applied to all layers produced the best mean intersection over union of 0.8045. The performance has been benchmarked with 11 other state-of-the-art deep semantic segmentation networks. For future work, the network performance can be increased by integrating synthetic augmented data to further optimize the training phase. Moreover, spatial and channel-based attention mechanisms can also be explored by emphasizing some strategic locations of the apples, which makes the recognition more accurate.

Photoacoustic imaging is used to differentiate between tissue types based on light absorption. Different structures, such as vascular density of capillaries in human tissue, can be analysed and provide diagnostic information to detect early stage breast cancer. Delay and sum (DAS) beamforming is the traditional method to reconstruct photoacoustic images. However, for structures located deep in the tissue (>10 mm), signal to noise (SNR) of the photoacoustic signal drops significantly. This study proposes using filter delay multiply and sum (FDMAS) beamforming technique to increase the SNR and enhance the image quality. Experimental results showed that FDMAS beamformer improved the SNR by 6.9 dB and the lateral resolution by 48% compared to the DAS beamformer. Moreover, the effect of aperture size on the proposed method is presented as the sub-group FDMAS, which further increased the improvement in image quality.

FDMAS has been successfully used in microwave imaging for breast cancer detection. FDMAS gained its popularity due to its capability to produce results faster than any other adaptive beamforming technique such as minimum variance (MV) which requires higher computational complexity. The average computational time for single point spread function (PSF) at 40 mm depth for FDMAS is 87 times faster than MV. The new beamforming technique has been tested on PSF and cyst phantoms experimentally with the ultrasound array research platform version 2 (UARP II) using a 3–8 MHz 128 element clinical transducer. FDMAS is able to improve both imaging contrast and spatial resolution as compared to DAS. The wire phantom main lobes lateral resolution improved in FDMAS by 40.4% with square pulse excitation signal when compared to DAS. Meanwhile the contrast ratio (CR) obtained for an anechoic cyst located at 15 mm depth for PWI with DAS and FDMAS are −6.2 dB and −14.9 dB respectively. The ability to reduce noise from off axis with auto-correlation operation in FDMAS pave the way to display the B-mode image with high dynamic range. However, the contrast to noise ratio (CNR) measured at same cyst location for FDMAS give less reading compared to DAS. Nevertheless, this drawback can be compensated by applying compound plane wave imaging (CPWI) technique on FDMAS. In overall the new FDMAS beamforming technique outperforms DAS in laboratory experiments by narrowing its main lobes and increases the image contrast without sacrificing its frame rates.