The increasing demand for food production due to the growing population is raising the need for more food-productive environments for plants. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to look after the individual plant component traits manually. Plant breeders need computer vision-based plant monitoring systems to analyze different plants' productivity and environmental suitability. It leads to performing feasible quantitative analysis, geometric analysis, and yield rate analysis of the plants. Many of the data collection methods have been used by plant breeders according to their needs. In the presented review, most of them are discussed with their corresponding challenges and limitations. Furthermore, the traditional approaches of segmentation and classification of plant phenotyping are also discussed. The data limitation problems and their currently adapted solutions in the computer vision aspect are highlighted, which somehow solve the problem but are not genuine. The available datasets and current issues are enlightened. The presented study covers the plants phenotyping problems, suggested solutions, and current challenges from data collection to classification steps.
Esophagitis, cancerous growths, bleeding, and ulcers are typical symptoms of gastrointestinal disorders, which account for a significant portion of human mortality. For both patients and doctors, traditional diagnostic methods can be exhausting. The major aim of this research is to propose a hybrid method that can accurately diagnose the gastrointestinal tract abnormalities and promote early treatment that will be helpful in reducing the death cases. The major phases of the proposed method are: Dataset Augmentation, Preprocessing, Features Engineering (Features Extraction, Fusion, Optimization), and Classification. Image enhancement is performed using hybrid contrast stretching algorithms. Deep Learning features are extracted through transfer learning from the ResNet18 model and the proposed XcepNet23 model. The obtained deep features are ensembled with the texture features. The ensemble feature vector is optimized using the Binary Dragonfly algorithm (BDA), Moth-Flame Optimization (MFO) algorithm, and Particle Swarm Optimization (PSO) algorithm. In this research, two datasets (Hybrid dataset and Kvasir-V1 dataset) consisting of five and eight classes, respectively, are utilized. Compared to the most recent methods, the accuracy achieved by the proposed method on both datasets was superior. The Q_SVM's accuracies on the Hybrid dataset, which was 100%, and the Kvasir-V1 dataset, which was 99.24%, were both promising.
The skin is the human body's largest organ and its cancer is considered among the most dangerous kinds of cancer. Various pathological variations in the human body can cause abnormal cell growth due to genetic disorders. These changes in human skin cells are very dangerous. Skin cancer slowly develops over further parts of the body and because of the high mortality rate of skin cancer, early diagnosis is essential. The visual checkup and the manual examination of the skin lesions are very tricky for the determination of skin cancer. Considering these concerns, numerous early recognition approaches have been proposed for skin cancer. With the fast progression in computer-aided diagnosis systems, a variety of deep learning, machine learning, and computer vision approaches were merged for the determination of medical samples and uncommon skin lesion samples. This research provides an extensive literature review of the methodologies, techniques, and approaches applied for the examination of skin lesions to date. This survey includes preprocessing, segmentation, feature extraction, selection, and classification approaches for skin cancer recognition. The results of these approaches are very impressive but still, some challenges occur in the analysis of skin lesions because of complex and rare features. Hence, the main objective is to examine the existing techniques utilized in the discovery of skin cancer by finding the obstacle that helps researchers contribute to future research.
Computer-aided polyp segmentation is a crucial task that supports gastroenterologists in examining and resecting anomalous tissue in the gastrointestinal tract. The disease polyps grow mainly in the colorectal area of the gastrointestinal tract and in the mucous membrane, which has protrusions of micro-abnormal tissue that increase the risk of incurable diseases such as cancer. So, the early examination of polyps can decrease the chance of the polyps growing into cancer, such as adenomas, which can change into cancer. Deep learning-based diagnostic systems play a vital role in diagnosing diseases in the early stages. A deep learning method, Graft-U-Net, is proposed to segment polyps using colonoscopy frames. Graft-U-Net is a modified version of UNet, which comprises three stages, including the preprocessing, encoder, and decoder stages. The preprocessing technique is used to improve the contrast of the colonoscopy frames. Graft-U-Net comprises encoder and decoder blocks where the encoder analyzes features, while the decoder performs the features' synthesizing processes. The Graft-U-Net model offers better segmentation results than existing deep learning models. The experiments were conducted using two open-access datasets, Kvasir-SEG and CVC-ClinicDB. The datasets were prepared from the large bowel of the gastrointestinal tract by performing a colonoscopy procedure. The anticipated model outperforms in terms of its mean Dice of 96.61% and mean Intersection over Union (mIoU) of 82.45% with the Kvasir-SEG dataset. Similarly, with the CVC-ClinicDB dataset, the method achieved a mean Dice of 89.95% and an mIoU of 81.38%.
BACKGROUND: COVID-19 has posed unique challenges for adolescents in different dimensions of their life including education, home and social life, mental and physical health. Whether the impact is positive or negative, its significance on the overall shaping of adolescents' lives cannot be overlooked. The aim of the present study was to explore impacts of the pandemic on the adolescents' everyday lives in Pakistan. METHODS: Following ethical approval, this cross-sectional study was conducted through September to December, 2020 via an online survey on 842 adolescents with the mean age of 17.14 ± SD 1.48. Socio-demographic data and Epidemic Pandemic Impact Inventory-Adolescent Adaptation (EPII-A) was used to assess the multi-dimensional effects of the pandemic. RESULTS: Among the 842 participants, 84% were girls. Education emerged as the most negatively affected Pandemic domain (41.6-64.3%). Most of the adolescents (62.0-65.8%) had reported changes in responsibilities at home including increased time spent in helping family members. Besides, increase in workload of participants and their parents was prominent (41.8% & 47.6%). Social activities were mostly halted for approximately half (41-51%) of the participants. Increased screen time, decreased physical activity and sedentary lifestyle were reported by 52.7%, 46.3% and 40.7% respectively. 22.2-62.4% of the adolescents had a direct experience with quarantine, while 15.7% experienced death of a close friend or relative. Positive changes in their lives were endorsed by 30.5-62.4% respondents. Being male and older adolescents had significant association with negative impact across most domains (p<0.05). CONCLUSIONS: Results have shown that COVID-19 exert significant multidimensional impacts on the physical, psycho-social, and home related domains of adolescents that are certainly more than what the previous researches has suggested.
COVID-19/epidemiology , COVID-19/psychology , Adolescent , Cross-Sectional Studies , Education , Family , Female , Humans , Male , Pakistan/epidemiology , Sociodemographic Factors
As COVID-19 grips the world, many people are quarantined or isolated resulting in adverse consequences for the mental health of youth. This rapid review takes into account the impact of quarantine on mental health of children and adolescents, and proposes measures to improve psychological outcomes of isolation. Three electronic databases including PubMed, Scopus, and ISI Web of Science were searched. Two independent reviewers performed title and abstract screening followed by full-text screening. This review article included 10 studies. The seven studies before onset of COVID 19 about psychological impact of quarantine in children have reported isolation, social exclusion stigma and fear among the children. The most common diagnoses were acute stress disorder, adjustment disorder, grief, and post-traumatic stress disorder. Three studies during the COVID-19 pandemic reported restlessness, irritability, anxiety, clinginess and inattention with increased screen time in children during quarantine. These adverse consequences can be tackled through carefully formulated multilevel interventions.
