Analysis of Birth Data using Ensemble Modeling Techniques

Machine learning and data mining are being used in different fields like data analysis, prediction, image processing, etc., and particularly in healthcare. Over the past decade, several types of research have been carried out focusing on machine learning and data mining application to generate intuitions from historical data and make predictions about the results. Machine learning algorithms play a vital role in improving healthcare systems due to continuous research in machine learning applications. Several researchers have used algorithms of machine learning to develop systems for decision support, analyze clinical aspects, use historical data to extract useful information, make future predictions and categorize diseases, etc. to help physicians make better decisions. In this study, we used an ensemble modeling voting technique for the classification of the birth dataset. Ensemble models combine individual machine learning algorithms to improve the accuracy by predicting from the combined output of the base classifiers. Gradient boosting classifier (GBC), random forest (RF), bagging classifier (BC), and extra trees classifier (ETC) were used as base learners for making a voting ensemble model for the classification of the birth dataset. The results produced have shown that the voting classifier of support vector machine (SVM), random forest (RF), extra trees classifier, and bagging classifier has given the best results with the proportion of 94.78%, gradient boosting classifier has 84.39% accuracy, the random forest has 94.26% accuracy, extra trees classifier have 94.02% accuracy and bagging classifier has 93.65% accuracy. The accuracy achieved by ensemble modeling is far higher than the machine learning algorithms. Ensemble models increase the accuracy of machine learning algorithms by reducing variance and classification errors. The development of such a system will not only help health organizations to take effective measures to improve the maternal health assessment process but will also open the doors for interdisciplinary research in two different fields in the region

State-of-the-Art CNN Optimizer for Brain Tumor Segmentation in Magnetic Resonance Images

Brain tumors have become a leading cause of death around the globe. The main reason for this epidemic is the difficulty conducting a timely diagnosis of the tumor. Fortunately, magnetic resonance images (MRI) are utilized to diagnose tumors in most cases. The performance of a Convolutional Neural Network (CNN) depends on many factors (i.e., weight initialization, optimization, batches and epochs, learning rate, activation function, loss function, and network topology), data quality, and specific combinations of these model attributes. When we deal with a segmentation or classification problem, utilizing a single optimizer is considered weak testing or validity unless the decision of the selection of an optimizer is backed up by a strong argument. Therefore, optimizer selection processes are considered important to validate the usage of a single optimizer in order to attain these decision problems. In this paper, we provides a comprehensive comparative analysis of popular optimizers of CNN to benchmark the segmentation for improvement. In detail, we perform a comparative analysis of 10 different state-of-the-art gradient descent-based optimizers, namely Adaptive Gradient (Adagrad), Adaptive Delta (AdaDelta), Stochastic Gradient Descent (SGD), Adaptive Momentum (Adam), Cyclic Learning Rate (CLR), Adaptive Max Pooling (Adamax), Root Mean Square Propagation (RMS Prop), Nesterov Adaptive Momentum (Nadam), and Nesterov accelerated gradient (NAG) for CNN. The experiments were performed on the BraTS2015 data set. The Adam optimizer had the best accuracy of 99.2% in enhancing the CNN ability in classification and segmentation

GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction

Generative adversarial networks (GAN), which are fueled by deep learning, are an efficient technique for image reconstruction using under-sampled MR data. In most cases, the performance of a particular model’s reconstruction must be improved by using a substantial proportion of the training data. However, gathering tens of thousands of raw patient data for training the model in actual clinical applications is difficult because retaining k-space data is not customary in the clinical process. Therefore, it is imperative to increase the generalizability of a network that was created using a small number of samples as quickly as possible. This research explored two unique applications based on deep learning-based GAN and transfer learning. Seeing as MRI reconstruction procedures go for brain and knee imaging, the proposed method outperforms current techniques in terms of signal-to-noise ratio (PSNR) and structural similarity index (SSIM). As compared to the results of transfer learning for the brain and knee, using a smaller number of training cases produced superior results, with acceleration factor (AF) 2 (for brain PSNR (39.33); SSIM (0.97), for knee PSNR (35.48); SSIM (0.90)) and AF 4 (for brain PSNR (38.13); SSIM (0.95), for knee PSNR (33.95); SSIM (0.86)). The approach that has been described would make it easier to apply future models for MRI reconstruction without necessitating the acquisition of vast imaging datasets