The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder–decoder is named SegNet, and consists of a hierarchical correspondence of encode–decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase.

This paper represents a different way of denoising lower limb Surface electromyography sEMG signals using Daubechies wavelets Much noise will be needed to remove as we can from this signal for it to function properly. The previous works couldn’t accurately determine the most suitable method to be used for lower limbs. This paper uses different thresholding approaches to calculate the highest value of SNR to identify the best denoising method. And a complete detailed survey of denoising techniques for reducing noise from surface electromyography signals is provided. This research has important implications for the practical application of lower limb EMG. This paper aimed to ascertain what are the most optimal parameters to be applied while using wavelet transform (Daubechies wavelets) to achieve the highest possible SNR in sEMG of the lower limb. The sample that was used came from 11 healthy subjects doing 3 different movements, using 4 electrodes to extract the signal. To identify the best denoising is calculated using different thresholding types, Daubechies level, and noise structure. The result from this experiment indicates that the hard-rigorous SURE threshold and scaled white noise provide the highest SNR in every signal tested but the Daubechies level differs from one signal to another.