Mask region-based convolutional neural network and VGG-16 inspired brain tumor segmentation.

The process of brain tumour segmentation entails locating the tumour precisely in images. Magnetic Resonance Imaging (MRI) is typically used by doctors to find any brain tumours or tissue abnormalities. With the use of region-based Convolutional Neural Network (R-CNN) masks, Grad-CAM and transfer learning, this work offers an effective method for the detection of brain tumours. Helping doctors make extremely accurate diagnoses is the goal. A transfer learning-based model has been suggested that offers high sensitivity and accuracy scores for brain tumour detection when segmentation is done using R-CNN masks. To train the model, the Inception V3, VGG-16, and ResNet-50 architectures were utilised. The Brain MRI Images for Brain Tumour Detection dataset was utilised to develop this method. This work's performance is evaluated and reported in terms of recall, specificity, sensitivity, accuracy, precision, and F1 score. A thorough analysis has been done comparing the proposed model operating with three distinct architectures: VGG-16, Inception V3, and Resnet-50. Comparing the proposed model, which was influenced by the VGG-16, to related works also revealed its performance. Achieving high sensitivity and accuracy percentages was the main goal. Using this approach, an accuracy and sensitivity of around 99% were obtained, which was much greater than current efforts.

Design of modified reference phase modulation based boost chopper fed fifteen level stepped DC link hybrid converter.

A new fifteen-level stepped DC to AC hybrid converter is proposed for Solar Photovoltaic (SPV) applications. A boost chopper circuit is designed and interfaced with the fifteen-level hybrid converters specific to Electric Vehicles' Brushless DC Motor (BLDC) drive systems. In chopper units, the output of solar panels is regulated and stepped up to obtain the nominal output voltage. In the stepped DC-link hybrid converter configuration, fifteen-level DC-link voltage is achieved by the series-operated DC-link modules with reduced electrical energy compression. From the comprehensive structure, it is anecdotal that the proposed topology has achieved minimum switching and power loss. Elimination of end passive components highlights the merits of the proposed hybrid systems. The reduction of controlled power semiconductor switches and gate-firing circuits has made the system more reliable than other hybrid converters. From the extensive analysis, the experimental setup has reported that 7% reduction in harmonics and a 54% reduction in controlled power switches than the existing fifteen-level converter topologies. Mitigation of power quality issues in the voltage profile of a fifteen-level multilevel hybrid converter is achieved through the implementation of dsPIC digital-controller-based gate triggering circuits.