Gliomas are often difficult to find and distinguish using typical manual segmentation approaches because of their vast range of changes in size, shape, and appearance. Furthermore, the manual annotation of cancer tissue segmentation under the close supervision of a human professional is both time-consuming and exhausting to perform. It will be easier and faster in the future to get accurate and quick diagnoses and treatments thanks to automated segmentation and survival rate prediction models that can be used now. In this article, a segmentation model is designed using RCNN that enables automatic prognosis on brain tumors using MRI. The study adopts a U-Net encoder for capturing the features during the training of the model. The feature extraction extracts geometric features for the estimation of tumor size. It is seen that the shape, location, and size of a tumor are significant factors in the estimation of prognosis. The experimental methods are conducted to test the efficacy of the model, and the results of the simulation show that the proposed method achieves a reduced error rate with increased accuracy than other methods.

The 5G technologies and OFDM introduce a substantial element of latency in the baseband Massive MIMO system. To declaim the low delay demand of multiple input and multiple outputs, a Fast Fourier Transform (FFT) and also consequent implementation was proposed. The main idea of this proposed system is to utilize the VLSI chip routing technology and reduce computations, processing time, and low latency. This proposed system is to reduce the number of computational complexities in the downlink and reorder the uplink. In OFDM implementation, the chip area of FFTs and IFFTs is occupied by memories, and these memories can be extracted using registers or RAM. An efficient data programming approach for memories and butterflies has been developed using embedded VLSI technology with multiple inputs and outputs (MIMO), known as mass embedded MIMO systems. Using this proposed scheme (Integrated Massive MIMO), N point FFT/IFFT processor design achieves a better throughput and lowest latency than for single-input pipelined FFT or IFFT architectures. In an N-point FFT/IFFT, the introduced scheme using VLSI Technology leads to more reduction in the latency. This N-point FFT/IFFT implementation is named “Integrated Massive MIMO Systems” (IMMS).