Comparative analysis of spatial and transform domain methods for meningioma subtype classification

Pattern recognition in histopathological image analysis requires new techniques and methods. Various techniques have been presented and some state of the art techniques have been applied to complex textural data in histological images. In this paper, we compare the novel Adaptive Discriminant Wavelet Packet Transform (ADWPT) with a few prominent techniques in texture analysis namely Local Binary Patterns (LBP), Grey Level Co-occurrence Matrices (GLCMs) and Gabor Transforms. We show that ADWPT is a better technique for Meningioma subtype classification and produces classification accuracies of as high as 90%

A robust adaptive wavelet-based method for classification of meningioma histology images

Intra-class variability in the texture of samples is an important problem in the domain of histological image classification. This issue is inherent to the field due to the high complexity of histology image data. A technique that provides good results in one trial may fail in another when the test and training data are changed and therefore, the technique needs to be adapted for intra-class texture variation. In this paper, we present a novel wavelet based multiresolution analysis approach to meningioma subtype classification in response to the challenge of data variation.We analyze the stability of Adaptive Discriminant Wavelet Packet Transform (ADWPT) and present a solution to the issue of variation in the ADWPT decomposition when texture in data changes. A feature selection approach is proposed that provides high classification accuracy

Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers

Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes

Plant Disease Detection and Classification by Deep Learning

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly

Correlation of red cell distribution width with inflammatory markers and its prognostic value in patients with diabetes and coronary artery disease

Background. Recent studies have shown red blood cell distribution width (RDW) as a marker for severity and prognosis in coronary artery disease patients. Other studies have also correlated RDW with diabetes mellitus and inflammation. However, such correlation and prognosis in patients with concomitant coronary artery disease and diabetes after percutaneous intervention remains unclear. Material and methods. Our study group comprised of 730 subjects including 700 patients (cases) and 30 normal subjects (control group). Patients who presented with coronary artery disease were divided into diabetic and non-diabetic groups. All patients had RDW measured at admission and percutaneous intervention was done. Follow-up for adverse events was carried out between 6 to 12 months. Results. RDW was elevated in patients as compared to control group (p < 0.05). RDW correlated well with inflammatory markers including erythrocyte sedimen­tation rate, C-reactive protein, HbA1c, white blood cells and troponin. RDW was higher with more severe atherosclerosis based on SYNTAX and Gensini scores (p < 0.05). Prognosis was found to be worse in patients with high RDW as well as in diabetics. Conclusions. RDW has positive correlation with other inflammatory marker. It may be used as a marker in determining the severity and prognosis in diabetic patients with coronary artery disease

Weed Detection by Faster RCNN Model: An Enhanced Anchor Box Approach

To apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studies. This research aimed to achieve a high average precision (AP) of eight classes of weeds and a negative (non-weed) class, using the DeepWeeds dataset. In this regard, a DL-based two-step methodology has been proposed. This article is the second stage of the research, while the first stage has already been published. The former phase presented a weed detection pipeline and consisted of the evaluation of various neural networks, image resizers, and weight optimization techniques. Although a significant improvement in the mean average precision (mAP) was attained. However, the Chinee apple weed did not reach a high average precision. This result provided a solid ground for the next stage of the study. Hence, this paper presents an in-depth analysis of the Faster Region-based Convolutional Neural Network (RCNN) with ResNet-101, the best-obtained model in the past step. The architectural details of the Faster RCNN model have been thoroughly studied to investigate each class of weeds. It was empirically found that the generation of anchor boxes affects the training and testing performance of the Faster RCNN model. An enhancement to the anchor box scales and aspect ratios has been attempted by various combinations. The final results, with the addition of 64 × 64 scale size, and aspect ratio of 1:3 and 3:1, produced the best classification and localization of all classes of weeds and a negative class. An enhancement of 24.95% AP was obtained in Chinee apple weed. Furthermore, the mAP was improved by 2.58%. The robustness of the approach has been shown by the stratified k-fold cross-validation technique and testing on an external dataset

Image-Based Plant Disease Identification by Deep Learning Meta-Architectures

The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the complex tasks of localization and classification of the disease in plant leaves. In this regard, three DL meta-architectures including the Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Networks (RFCN) were applied by using the TensorFlow object detection framework. All the DL models were trained/tested on a controlled environment dataset to recognize the disease in plant species. Moreover, an improvement in the mean average precision of the best-obtained deep learning architecture was attempted through different state-of-the-art deep learning optimizers. The SSD model trained with an Adam optimizer exhibited the highest mean average precision (mAP) of 73.07%. The successful identification of 26 different types of defected and 12 types of healthy leaves in a single framework proved the novelty of the work. In the future, the proposed detection methodology can also be adopted for other agricultural applications. Moreover, the generated weights can be reused for future real-time detection of plant disease in a controlled/uncontrolled environment

Change in the spectrum of orthopedic trauma: Effects of COVID-19 pandemic in a developing nation during the upsurge; a cross-sectional study

Background: The COVID-19 pandemic has caused a great impact on orthopedic surgery with a significant curtailment in elective surgeries which is the major bread and butter for orthopedic surgeons. It was also observed that the spectrum of orthopedic trauma injuries has shifted from more severe and frequent road traffic accidents (high energy trauma) to general, low energy house-hold injuries like low energy fractures in the elderly, pediatric fractures, house-hold sharp cut injuries and nail bed lacerations. The aim of this study is to appraise the effect of the COVID-19 pandemic on orthopedic surgical practice, both inpatient and outpatient facility.Materials and methods: This is a retrospective cross sectional study conducted in a tertiary care teaching hospital. We collected data of patients admitted from February 1, 2020 to 30th April 2020 in the orthopedic service line using non-probability consecutive sampling. This study population was divided into pre-COVID and COVID eras (6 weeks each). The data included patient demographic parameters like age, gender and site of injury, mechanism of injury, diagnosis and procedure performed and carrying out of COVID-19 Polymerase Chain Reaction (PCR) test in the COVID-era.Results: We observed that outpatient clinical volume decreased by 75% in COVID era. Fifty percent of surgical procedures decreased in COVID era as compared to pre-COVID era. Trauma procedures reduced by 40% in COVID era. Most common mechanism of injury was household injuries like low energy falls. A significant reduction in elective surgeries by 67% was observed in the COVID era.Conclusion: The impact of COVID-19 pandemic has significantly changed the spectrum of orthopedic injury. More household injuries have occurred and are anticipated due to the ongoing effects of lockdown

Weed Identification by Single-Stage and Two-Stage Neural Networks: A Study on the Impact of Image Resizers and Weights Optimization Algorithms

The accurate identification of weeds is an essential step for a site-specific weed management system. In recent years, deep learning (DL) has got rapid advancements to perform complex agricultural tasks. The previous studies emphasized the evaluation of advanced training techniques or modifying the well-known DL models to improve the overall accuracy. In contrast, this research attempted to improve the mean average precision (mAP) for the detection and classification of eight classes of weeds by proposing a novel DL-based methodology. First, a comprehensive analysis of single-stage and two-stage neural networks including Single-shot MultiBox Detector (SSD), You look only Once (YOLO-v4), EfficientDet, CenterNet, RetinaNet, Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Network (RFCN), has been performed. Next, the effects of image resizing techniques along with four image interpolation methods have been studied. It led to the final stage of the research through optimization of the weights of the best-acquired model by initialization techniques, batch normalization, and DL optimization algorithms. The effectiveness of the proposed work is proven due to a high mAP of 93.44% and validated by the stratified k-fold cross-validation technique. It was 5.8% improved as compared to the results obtained by the default settings of the best-suited DL architecture (Faster RCNN ResNet-101). The presented pipeline would be a baseline study for the research community to explore several tasks such as real-time detection and reducing the computation/training time. All the relevant data including the annotated dataset, configuration files, and inference graph of the final model are provided with this article. Furthermore, the selection of the DeepWeeds dataset shows the robustness/practicality of the study because it contains images collected in a real/complex agricultural environment. Therefore, this research would be a considerable step toward an efficient and automatic weed control system