Deep Learning Framework to Detect Ischemic Stroke Lesion in Brain MRI Slices of Flair/DW/T1 Modalities

Ischemic stroke lesion (ISL) is a brain abnormality. Studies proved that early detection and treatment could reduce the disease impact. This research aimed to develop a deep learning (DL) framework to detect the ISL in multi-modality magnetic resonance image (MRI) slices. It proposed a convolutional neural network (CNN)-supported segmentation and classification to execute a consistent disease detection framework. The developed framework consisted of the following phases; (i) visual geometry group (VGG) developed VGG16 scheme supported SegNet (VGG-SegNet)-based ISL mining, (ii) handcrafted feature extraction, (iii) deep feature extraction using the chosen DL scheme, (iv) feature ranking and serial feature concatenation, and (v) classification using binary classifiers. Fivefold cross-validation was employed in this work, and the best feature was selected as the final result. The attained results were separately examined for (i) segmentation; (ii) deep-feature-based classification, and (iii) concatenated feature-based classification. The experimental investigation is presented using the Ischemic Stroke Lesion Segmentation (ISLES2015) database. The attained result confirms that the proposed ISL detection framework gives better segmentation and classification results. The VGG16 scheme helped to obtain a better result with deep features (accuracy > 97%) and concatenated features (accuracy > 98%)

Feature Evaluation of Emerging E-Learning Systems Using Machine Learning: An Extensive Survey

As of late, with the progression of AI and man-made brainpower, there has been a developing spotlight on versatile e-learning. As all ways to deal with e-learning lose their allure and the level of online courses builds, they move towards more customized versatile learning so as to collaborate with students and achieve better learning results. The schools focus on the examination, mindfulness, and arranging techniques that infuse innovation into the vision and educational program. E-learning issues are a standard examination issue for us all. The motivation behind this research analysis is to separate the potential outcomes of assessing e-learning models utilizing AI strategies such as Supervised, Semi Supervised, Reinforced Learning advances by investigating upsides and downsides of various methods organization. The literature review methodology is to review the cross sectional impacts of e-learning and Machine learning algorithms from existing literatures from the year 1993 to 2020 and to assess the essentialness of e-learning features to optimize the e-learning models with available Machine learning techniques from peer-inspected journals, capable destinations, and books. Second, it legitimizes the chances of e-learning structures introduction, and changes demonstrated through AI and Machine Learning algorithms. This examination assists in providing helpful new highlights to analysts, researchers and academicians. It gives an exhaustive structure of existing e-learning frameworks for the most recent innovations identified with learning framework capacities and learning tasks to envision ML research openings in appropriate spaces. The survey paper identifies and demonstrates the important role of different types of e-learning features such as Individual pertinent feature, Course pertinent feature, Context pertinent feature and Technology pertinent feature in framework performance tuning. The performance of Machine Learning algorithms to optimize the features of E-Learning models were reviewed in previous literatures and Support Vector Machine technique was found to be the one of the best to predict the input and output parameters of e-learning models and it is found that Fuzzy C Means, Deep Learning algorithms are producing better results for Big Data sets

Automated Detection of Retinopathy of Prematurity Using Quantum Machine Learning and Deep Learning Techniques

Retinopathy of prematurity (ROP) is a vasoproliferative retinal disease that affects premature infants and causes permanent blindness if left untreated. Automated retinal diagnosis from the Retinal fundus images aid in the early detection of many pathological conditions. The low-level statistical features used in literatures have not provided the complete ROP-specific profile, and hence it has to be replaced by high-level features. The proposed system involves extracting Scale Invariant Feature Transform (SIFT) - Speeded Up Robust Features (SURF) combined high-level features from the SegNet segmented retinal vessels and classified using the Quantum Support Vector Machine (QSVM) classifier. This study aims (i) to segment retinal vessels from the acquired fundus images using SegNet and extract their features using the SURF and SIFT Feature Extraction method, (ii) to classify the Normal and ROP retinal vessels using four classical machine learning classifiers such as Support Vector Machine (SVM), Reduced Error Pruning (REP) tree, K-Star, and LogitBoost and Quantum SVM classifier, (iii) to develop a novel transformer-based Swin-T ROP model to classify ROP from normal Neonatal fundus images, (iv) to compare the performance characteristics of the proposed QSVM model with the Resnet50, DarkNet19, and classical machine learning classifiers. The study is conducted using 200 fundus images, including 100 normal and 100 ROP-positive neonatal retinal images. The machine learning classifiers such as SVM, REP Tree, K-Star, and Logit Boost Classifiers attained accuracy of 86.7%, 75%, 74%, and 76.5%, respectively, in classifying ROP from normal retinal images. The deep learning networks such as ResNet50 and DarkNet19 classified ROP from normal fundus images with an accuracy of 92.87% and 89%, respectively. The Quantum machine learning classifier outperforms the classical machine learning classifiers, Pre-trained Convolutional Neural Networks (CNN) and SwinT-ROP in terms of classification accuracy (95.5%), sensitivity (93%), and specificity (98%). The proposed system accurately diagnoses ROP from the neonatal fundus images and could be used in point-of-care diagnosis to access diagnostic expertise in underserved regions

Effect of Antidepressant Switching vs Augmentation on Remission Among Patients With Major Depressive Disorder Unresponsive to Antidepressant Treatment: The VAST-D Randomized Clinical Trial.

ImportanceLess than one-third of patients with major depressive disorder (MDD) achieve remission with their first antidepressant.ObjectiveTo determine the relative effectiveness and safety of 3 common alternate treatments for MDD.Design, setting, and participantsFrom December 2012 to May 2015, 1522 patients at 35 US Veterans Health Administration medical centers who were diagnosed with nonpsychotic MDD, unresponsive to at least 1 antidepressant course meeting minimal standards for treatment dose and duration, participated in the study. Patients were randomly assigned (1:1:1) to 1 of 3 treatments and evaluated for up to 36 weeks.InterventionsSwitch to a different antidepressant, bupropion (switch group, n = 511); augment current treatment with bupropion (augment-bupropion group, n = 506); or augment with an atypical antipsychotic, aripiprazole (augment-aripiprazole group, n = 505) for 12 weeks (acute treatment phase) and up to 36 weeks for longer-term follow-up (continuation phase).Main outcomes and measuresThe primary outcome was remission during the acute treatment phase (16-item Quick Inventory of Depressive Symptomatology-Clinician Rated [QIDS-C16] score ≤5 at 2 consecutive visits). Secondary outcomes included response (≥50% reduction in QIDS-C16 score or improvement on the Clinical Global Impression Improvement scale), relapse, and adverse effects.ResultsAmong 1522 randomized patients (mean age, 54.4 years; men, 1296 [85.2%]), 1137 (74.7%) completed the acute treatment phase. Remission rates at 12 weeks were 22.3% (n = 114) for the switch group, 26.9% (n = 136)for the augment-bupropion group, and 28.9% (n = 146) for the augment-aripiprazole group. The augment-aripiprazole group exceeded the switch group in remission (relative risk [RR], 1.30 [95% CI, 1.05-1.60]; P = .02), but other remission comparisons were not significant. Response was greater for the augment-aripiprazole group (74.3%) than for either the switch group (62.4%; RR, 1.19 [95% CI, 1.09-1.29]) or the augment-bupropion group (65.6%; RR, 1.13 [95% CI, 1.04-1.23]). No significant treatment differences were observed for relapse. Anxiety was more frequent in the 2 bupropion groups (24.3% in the switch group [n = 124] vs 16.6% in the augment-aripiprazole group [n = 84]; and 22.5% in augment-bupropion group [n = 114]). Adverse effects more frequent in the augment-aripiprazole group included somnolence, akathisia, and weight gain.Conclusions and relevanceAmong a predominantly male population with major depressive disorder unresponsive to antidepressant treatment, augmentation with aripiprazole resulted in a statistically significant but only modestly increased likelihood of remission during 12 weeks of treatment compared with switching to bupropion monotherapy. Given the small effect size and adverse effects associated with aripiprazole, further analysis including cost-effectiveness is needed to understand the net utility of this approach.Trial registrationclinicaltrials.gov Identifier: NCT01421342