Computer vision is becoming an increasingly trendy word in the area of image processing. With the emergence of computer vision applications, there is a significant demand to recognize objects automatically. Deep CNN (convolution neural network) has benefited the computer vision community by producing excellent results in video processing, object recognition, picture classification and segmentation, natural language processing, speech recognition, and many other fields. Furthermore, the introduction of large amounts of data and readily available hardware has opened new avenues for CNN study. Several inspirational concepts for the progress of CNN have been investigated, including alternative activation functions, regularization, parameter optimization, and architectural advances. Furthermore, achieving innovations in architecture results in a tremendous enhancement in the capacity of the deep CNN. Significant emphasis has been given to leveraging channel and spatial information, with a depth of architecture and information processing via multi-path. This survey paper focuses mainly on the primary taxonomy and newly released deep CNN architectures, and it divides numerous recent developments in CNN architectures into eight groups. Spatial exploitation, multi-path, depth, breadth, dimension, channel boosting, feature-map exploitation, and attention-based CNN are the eight categories. The main contribution of this manuscript is in comparing various architectural evolutions in CNN by its architectural change, strengths, and weaknesses. Besides, it also includes an explanation of the CNN’s components, the strengths and weaknesses of various CNN variants, research gap or open challenges, CNN applications, and the future research direction.

BACKGROUND Cervical carotid artery injuries entail high morbidity and mortality and are technically challenging to repair. This retrospective study describes the management and outcomes of cervical carotid injuries sustained during the recent wars in Iraq and Afghanistan. METHODS The Department of Defense Trauma Registry was queried to identify US military personnel who sustained battle-related cervical carotid injury between January 2002 and December 2015. Retrospective chart reviews of the military Electronic Health Record were performed on patients identified. Demographics, injury characteristics, surgical management, and outcomes were reviewed. Statistical analysis was performed to identify associations between injury and management factors, as well as stroke and mortality. RESULTS In total, 67 patients (100% male; age, 25 ± 7 years) were identified with cervical carotid artery injuries. Fifty-six patients (84%) sustained a common carotid artery (CCA) or internal carotid artery (ICA) injury, and 11 patients (16%) had an isolated external carotid artery (ECA) injury. The anatomic distribution of injury was as follows: CCA, 26 (38.8%); ICA, 24 (35.8%); CCA and ICA 2 (3%); ICA and ECA 3 (4.5%); and CCA, ICA, and ECA 1 (1.5%). Of the 56 CCA or ICA injuries, 39 underwent vascular repair, 9 (16%) were managed with ligation, 1 was treated with a temporary vascular shunt but succumbed to injuries before vascular repair, and 7 (13%) were treated nonoperatively. Seven (23%) of 30 ICA injuries were ligated compared with 2 (7.7%) of 26 injuries isolated to the CCA (p = 0.02). Compared with repair, ligation of the CCA/ICA was associated with a higher rate of stroke (89% vs. 33%, p = 0.003) and increased mortality without statistical significance (22% vs. 10%, p = 0.3). Every patient who underwent ICA ligation had a stroke (7/7). There was no difference in Injury Severity Score between the ligation and repair groups (23.8 ± 10.6 vs. 24.7 ± 13.4, p = 0.9). At a mean follow-up of 34.5 months, 10 of 17 stroke survivors had permanent neurologic deficits. CONCLUSION In modern combat, penetrating injuries involving the cervical carotid arteries are relatively infrequent. In this experience, isolated ICA injuries were three times more likely to be ligated than those involving the CCA. As a surgical maneuver, ICA ligation resulted in stroke in all cases. LEVEL OF EVIDENCE Retrospective cohort analysis, level III.

Food image classification and recognition is an emerging research area due to its growing importance in the medical and health industries. As India is growing digitally rapidly, an automated Indian food image recognition system will help in the development of diet tracking, calorie estimation, and many other health and food consumption-related applications. In recent years, many deep learning techniques have evolved. Deep learning is a robust and low-cost method for extracting information from food images, though, challenges lie in extracting information from real-world food images due to various factors affecting image quality such as photos from different angles and positions, several objects appearing in the photo, etc. In this paper, we used CNN as our base model to build our system, which gives a system accuracy of 85%. After that, we deployed the transfer learning technique with MobileNetV3 for improvement in accuracy, which resulted in an improvement in accuracy of up to 93.3%. Furthermore, we applied data augmentation techniques in the preprocessing phase and we trained our model using transfer learning with MobileNetV3 and we obtained an accuracy of up to 95.3%. So, the accuracy of the model increases by applying the data augmentation technique in addition to transfer learning.

Precision and efficacy are vital in the constantly advancing field of food image identification, particularly in the domains of medicine and healthcare. Transfer learning and deep ensemble learning techniques are employed to enhance the accuracy and efficiency of the Indian Food Classification System. The ensemble model effectively captures various patterns and correlations within the information by employing many machine learning techniques. The ensemble method we employ utilizes the MobileNetV3 and DenseNet-121 transfer learning models to construct a robust model. The ensemble model benefits from the integration of model predictions, resulting in enhanced recognition of Indian food. The study utilized a dataset consisting of 6000 photographs of Indian cuisine, categorized into 26 distinct groups. The picture dataset is divided into two subsets: 80% is allocated for training and 20% is reserved for testing. The experimental results demonstrate that DenseNet-121 surpasses MobileNetv3 in terms of testing accuracy, achieving a rate of 90%. The MobileNetV3 model achieves an accuracy of 87.64% on the Indian food image dataset. The integration of both models in ensemble learning yields a model accuracy of 92.38%, surpassing the performance of each individual model. This research revolutionizes our food relationship with the use of state-of-the-art technologies. By utilizing the most advanced transfer learning algorithm specifically designed for the precise classification of Indian cuisine, our aim is to establish a new standard in both technology and gastronomy. This will facilitate innovation in food perception, comprehension, and engagement.