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.

INTRODUCTION: Accurate analysis of brain MRI images is vital for diagnosing brain tumor in its nascent stages. Automated classification of brain tumor is an important step for accurate diagnosis. OBJECTIVES: This paper propose a model named Artificially-integrated Convolutional Neural Networks (AiCNNs) that accurately classifies brain MRI scans to 3 classes of brain tumor and negative diagnosis results. METHODS: AiCNNs model integrates 5 already trained models including simple convolutional neural networks (one uses a simple CNN while the other utilizes data augmentation) and three pre-trained networks whose weights are transferred from ImageNet dataset. RESULTS: AiCNNs model was trained on 3501 augmented T1-weighted contrast enhanced MRI (CE-MRI) brain images. Validation results of 99.49% (loss=0.0303) had been achieved by AiCNNs on a set of 1167 images, which outperform its contemporaries which have got results upto 97.81% (loss=0.1794) and 97.79% (loss=0.1787). CONCLUSION: AiCNNs has been shown to obtained a test accuracy of 98.89 % on a set of 1167 images.

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AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction

A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer

Classification of Invasive Ductal Carcinoma from histopathology breast cancer images using Stacked Generalized Ensemble

Background: Breast Cancer is a leading cause of mortality and ranked apex among females all over the world. Early detection and diagnosis of breast cancer may reduce the increasing burden of the disease. With this background, a Population and Hospital based cancer registries have been studied to understand the epidemiology of breast cancer trends in India. Objective: The objective of this study is to gather information provided in different cancer registries and analyze the epidemiology of breast cancer statistics in India. Methods: Age Adjusted Rate, Age Specific Rate and Mortality Incidence Ratio has been considered as evaluation metric. Results: In this study it has been observed that the Age Adjusted Rate and Mortality rate of Breast Cancer has been reported as 25.8 and 12.7 per 100,000 women respectively. Conclusion: Additional awareness & screening programs early diagnosis and treatment facilities can only significantly improve the breast cancer’s clinical picture in India.

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Epidemiology of Breast Cancer in Indian Women: Population and Hospital Based study

Problems of online learning, or real-world scenarios where machines are required to be trained on batches of data instead of the entire superset, necessitate an efficient and accurate incremental learning approach which both, consumes less time and is computationally light; Incremental Support Vector Machines (SVM) has proved to be a promising panacea. We propose a novel approach, which firstly uses SVMs in an ensemble manner for learning from the batches of data, it then discards the correctly classified data and trains a new SVM on the misclassified data points, the weighted sums of the correctly trained SVMs, and the machines trained on the misclassified points, are then used to obtain the final classification values. Weight assignment to SVMs, for final classification at each step, is done using Particle Swam Optimization (PSO). The simulation results attest to the veracity of the model, and comparisons suggest that the proposed approach holds promise.

Deepika Kumar

Papers

AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction

A median based quadrilateral local quantized ternary pattern technique for the classification of dermatoscopic images of skin cancer

Classification of Invasive Ductal Carcinoma from histopathology breast cancer images using Stacked Generalized Ensemble

Epidemiology of Breast Cancer in Indian Women: Population and Hospital Based study

Novel approach for incremental learning using ensemble of SVMs with particle swarm optimization