Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects

Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning-based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation-based selection method and as the output, the best features are selected. These selected features are validated through feed-forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.

Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture

Diabetes is a dreadful disease identified by escalated levels of glucose in the blood Machine learning algorithms help in identification and prediction of diabetes at an early stage The main objective of this study is to predict diabetes mellitus with better accuracy using an ensemble of machine learning algorithms The Pima Indians Diabetes dataset has been considered for experimentation, which gathers details of patients with and without having diabetes The proposed ensemble soft voting classifier gives binary classification and uses the ensemble of three machine learning algorithms viz random forest, logistic regression, and Naive Bayes for the classification Empirical evaluation of the proposed methodology has been conducted with state-of-the-art methodologies and base classifiers such as AdaBoost, Logistic Regression,Support Vector machine, Random forest, Naive Bayes, Bagging, GradientBoost, XGBoost, CatBoost by taking accuracy, precision, recall, F1-score as the evaluation criteria The proposed ensemble approach gives the highest accuracy, precision, recall, and F1_score value with 7904%, 7348%, 7145% and 806% respectively on the PIMA diabetes dataset Further, the efficiency of the proposed methodology has also been compared and analysed with breast cancer dataset The proposed ensemble soft voting classifier has given 9702% accuracy on the breast cancer dataset

An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier

Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges.

Deep learning for chest X-ray analysis: A survey.

Depression is a medical illness that affects an individual negatively by changing the way one feels, think and act but luckily, it's treatable, so the problem is its detection which we can solve with the help of machine learning. A model is developed to detect whether a person is suffering from depression or not using the prosodic features (pitch, tone, rhythm) of their voice which are promising indicators of depression. The model is trained by the data provided by the DAIC-WOZ that contains clinical interviews taken by the virtual interviewer called Ellie, which is then preprocessed using sox and system programming to remove the voice of virtual interviewer. Feature extraction is done by making spectrograms of each audio file using Librosa library. Then these spectrograms are passed into a convolution neural network with average pooling layers, dropout, He-initialisation, batch normalization, exponential linear unit activation function, and Nesterov accelerated gradients optimizer.It has been observed from the experiments that the prediction is made with an average F1 score of 0.93.

A Novel Approach for Depression Detection Using Audio Sentiment Analysis

Musculoskeletal abnormality detection serves as an advantage to the professionals in the medical domain and also serves as an assistance in the diagnosis as well as the treatment of the abnormalities. This paper mainly focuses on accurately detecting musculoskeletal abnormalities using various deep learning models and techniques. MURA dataset has been used for experimentation. MURA dataset has 14,863 images of finger, wrist, elbow, shoulder, forearm and hand which has been analyzed using deep learning models. In this research paper, authors have proposed GnCNNr model which utilizes group normalization, weight standardization and cyclic learning rate scheduler to enhance the accuracy, precision and other model interpretation metrics. The musculoskeletal abnormality has been detected by using various deep learning models. Accuracy and Cohen Kappa have been taken as the evaluation criteria. The highest accuracy of 85% and Cohen Kappa statistic of 0.698 was achieved by the GnCNNr model in comparison with the conventional deep learning methods like DenseNet, Inception, Inception v2 model.

https://link.springer.com/content/pdf/10.1007/s42979-020-00340-7.pdf

Musculoskeletal Abnormality Detection in Medical Imaging Using GnCNNr (Group Normalized Convolutional Neural Networks with Regularization)

Image captioning is oriented towards describing an image with the best possible use of words that can provide a semantic, relatable meaning of the scenario inscribed. Different models can be used to accomplish this arduous task depending on the context and requirement of what needs to be achieved. An encoder–decoder model which uses the image feature vectors as an input to the encoder is often marked as one of the appropriate models to accomplish the captioning process. In the proposed work, a dual-modal transformer has been used which captures the intra- and inter-model interactions in a simultaneous manner within an attention block. The transformer architecture is quantitatively evaluated on a publicly available Microsoft Common Objects in Context (MS COCO) dataset yielding a Bilingual Evaluation Understudy (BLEU)-4 Score of 85.01. The efficacy of the model is evaluated on Flickr 8k, Flickr 30k datasets and MS COCO datasets and results for the same is compared and analysed with the state-of-the-art methods. The results shows that the proposed model outperformed when compared with conventional models, such as the encoder–decoder model and attention model.

/pdf/dual-modal-transformer-with-enhanced-inter-and-intra-1ziqgck4.pdf

Dual-Modal Transformer with Enhanced Inter- and Intra-Modality Interactions for Image Captioning

Traditionally, searching for videos on popular streaming sites like YouTube is performed by taking the keywords, titles, and descriptions that are already tagged along with the video into consideration. However, the video content is not utilized for searching of the user’s query because of the difficulty in encoding the events in a video and comparing them to the search query. One solution to tackle this problem is to encode the events in a video and then compare them to the query in the same space. A method of encoding meaning to a video could be video captioning. The captioned events in the video can be compared to the query of the user, and we can get the optimal search space for the videos. There have been many developments over the course of the past few years in modeling video-caption generators and sentence embeddings. In this paper, we exploit an end-to-end video captioning model and various sentence embedding techniques that collectively help in building the proposed video-searching method. The YouCook2 dataset was used for the experimentation. Seven sentence embedding techniques were used, out of which the Universal Sentence Encoder outperformed over all the other six, with a median percentile score of 99.51. Thus, this method of searching, when integrated with traditional methods, can help improve the quality of search results.

Video Caption Based Searching Using End-to-End Dense Captioning and Sentence Embeddings

• The COVID outbreak of 2019 is the biggest crisis human civilisation has faced in recent history. A virus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was the cause of this outbreak. • Various techniques have been used in multiple studies to predict the severity and mortality of patients ranging from their race, chest CT scans, medical biomarkers, and past medical records. • The model proposed in the study determines the probability of death of a patient admitted to a hospital taking into account various aspects of the patient such as age and the blood biomarkers. • The proposed model uses a soft voting classifier to predict the mortality of the patient admitted to the hospital with base estimators such as Random Forest, XGBoost, Gradient Boosting Classifier, and Extra Tree Classifier. • Accuracy, Precision, Recall, and F1 Score have been used as the evaluation criteria for checking the robustness of the proposed methodology. • Comparing the proposed model with base models and the existing state of the art models provides a superior F1 score which is crucial during the COVID waves. COVID-19 is a novel coronavirus that spread around the globe with the initial reports coming from Wuhan, China, turned into a pandemic and caused enormous casualties. Various countries have faced multiple COVID spikes which put the medical infrastructure of these countries under immense pressure with third-world countries being hit the hardest. It can be thus concluded that determining the likeliness of death of a patient helps in avoiding fatalities which inspired the authors to research the topic. There are various ways to approach the problem such as past medical records, chest X-rays, CT scans, and blood biomarkers. Since blood biomarkers are most easily available in emergency scenarios, blood biomarkers were used as the features for the model. The data was first imputed and the training data was oversampled to avoid class imbalance in the model training. The model is composed of a voting classifier that takes in outputs from multiple classifiers. The model was then compared to base models such as Random Forest, XGBoost, and Extra Trees Classifier on multiple evaluation criteria. The F1 score was the concerned evaluation criterion as it maximizes the use of the medical infrastructure with the minimum possible casualties by maximizing true positives and minimizing false negatives. 

https://doi.org/10.1016/j.ijcce.2022.09.001

Deepika Kumar

Papers

A Novel Approach for Depression Detection Using Audio Sentiment Analysis

Musculoskeletal Abnormality Detection in Medical Imaging Using GnCNNr (Group Normalized Convolutional Neural Networks with Regularization)

Dual-Modal Transformer with Enhanced Inter- and Intra-Modality Interactions for Image Captioning

Video Caption Based Searching Using End-to-End Dense Captioning and Sentence Embeddings

Mortality prediction of COVID-19 patients using soft voting classifier