In biomedical image analysis, information about the location and appearance of tumors and lesions is indispensable to aid doctors in treating and identifying the severity of diseases. Therefore, it is essential to segment the tumors and lesions. MRI, CT, PET, ultrasound, and X-ray are the different imaging systems to obtain this information. The well-known semantic segmentation technique is used in medical image analysis to identify and label regions of images. The semantic segmentation aims to divide the images into regions with comparable characteristics, including intensity, homogeneity, and texture. UNET is the deep learning network that segments the critical features. However, UNETs basic architecture cannot accurately segment complex MRI images. This review introduces the modified and improved models of UNET suitable for increasing segmentation accuracy.

Leakage current of CMOS circuits has become a major factor in very deep submicron regime. ITRS reports that leakage power dissipation is rapidly becoming a substantial contributor to the total power dissipation as threshold voltage becomes small. In this paper a leakage reduction technique named "Super stack" for sub 0.5-V supply voltage has been proposed. Super Stack technique comes in handy where Multithreshold techniques fail to apply for 0.5-V or lower supply voltages. The proposed method can be used in sub 0.5-V supply voltage for reducing the leakage power in active mode and standby mode while reducing the delay.

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