Cataract is one of the most common eye disorders that causes vision distortion. Accurate and timely detection of cataracts is the best way to control the risk and avoid blindness. Recently, artificial intelligence-based cataract detection systems have been received research attention. In this paper, a novel deep neural network, namely CataractNet , is proposed for automatic cataract detection in fundus images. The loss and activation functions are tuned to train the network with small kernels, fewer training parameters, and layers. Thus, the computational cost and average running time of CataractNet are significantly reduced compared to other pre-trained Convolutional Neural Network (CNN) models. The proposed network is optimized with the Adam optimizer. A total of 1130 cataract and non-cataract fundus images are collected and augmented to 4746 images to train the model. For avoiding the over-fitting problem, the dataset is extended through augmentation before model training. Experimental results prove that the proposed method outperforms the state-of-the-art cataract detection approaches with an average accuracy of 99.13%.

Mobile communication systems are always in continuous evolution due to the demands of the end-users using this technology. Therefore, before the possible launch of 5G, some technologies have opened the way to the new mobile communication system. The need for technologies that provide more comfort to users has led to the construction of complex communication systems that were only science fiction decades ago. The information society in which we are now immersed has been the result of constant progress over time. In this paper, a survey of multiple access schemes for next-generation wireless communication systems is presented. Multiple access schemes are reviewed for possible use in next-generation wireless communication systems such as orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA), and delta-orthogonal multiple access (D-OMA), etc. General comparisons of 1G to 6G are presented. Different types of OMA are explained, and then orthogonal frequency division multiple access (OFDMA) is chosen as an example of the OMA scheme to compare with NOMA and D-OMA. There are two types of NOMA: power-domain and code-domain, which are discussed and compared. Simulation results are presented, and a comparison among different access schemes is provided.

Acne scarring occurs in 95% of people with acne vulgaris due to collagen loss or gains when the body is healing the damages of the skin caused by acne inflammation. Accurate classification of acne scars is a vital factor in providing a timely, effective treatment protocol. Dermatologists mainly recognize the type of acne scars manually based on visual inspections, which are time- and energy-consuming and subject to intra- and inter-reader variability. In this paper, a novel automated acne scar classification system is proposed based on a deep Convolutional Neural Network (CNN) model. First, a dataset of 250 images from five different classes is collected and labeled by four well-experienced dermatologists. The pre-processed input images are fed into our proposed model, namely ScarNet , for deep feature map extraction. The optimizer, loss function, activation functions, filter and kernel sizes, regularization methods, and the batch size of the proposed architecture are tuned so that the classification performance is maximized while minimizing the computational cost. Experimental results demonstrate the feasibility of the proposed method with accuracy, specificity, and kappa score of 92.53%, 95.38%, and 76.7%, respectively.

Clinical screening with low-quality fundus images is challenging and significantly leads to misdiagnosis. This paper addresses the issue of improving the retinal image quality and vessel segmentation through retinal image restoration. More specifically, a cycle-consistent generative adversarial network (CycleGAN) with a convolution block attention module (CBAM) is used for retinal image restoration. A modified UNet is used for retinal vessel segmentation for the restored retinal images (CBAM-UNet). The proposed model consists of two generators and two discriminators. Generators translate images from one domain to another, i.e., from low to high quality and vice versa. Discriminators classify generated and original images. The retinal vessel segmentation model uses downsampling, bottlenecking, and upsampling layers to generate segmented images. The CBAM has been used to enhance the feature extraction of these models. The proposed method does not require paired image datasets, which are challenging to produce. Instead, it uses unpaired data that consists of low- and high-quality fundus images retrieved from publicly available datasets. The restoration performance of the proposed method was evaluated using full-reference evaluation metrics, e.g., peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The retinal vessel segmentation performance was compared with the ground-truth fundus images. The proposed method can significantly reduce the degradation effects caused by out-of-focus blurring, color distortion, low, high, and uneven illumination. Experimental results show the effectiveness of the proposed method for retinal image restoration and vessel segmentation.