Currently, cancer is the number one killer among diseases affecting the global population. Thus, the detection of these kinds of tumors at an early stage means less sufferance and a longer life expectation. This study employs tomography to aid computerized diagnosis and diagnosis of breast tumors as either benign or malignant. Three-dimensional segmentation of heart in the LiTS17 dataset and applying Fully Connected Neural Network (FCN) for detecting the current tumor and to estimate it as benign or malignant tumor. In this study, we present a new method, which is a type of FCN including 23 layers used to segment the liver into three parts. It is applied in two ways in the first method; FCN is applied on the EIT using the deep learning algorithm that has an accuracy rate of 82. 95% accuracy. The second is more accurate, with better results obtained by using U-Net and SegNet, 98. 85% and 98. 25% accuracy, respectively. The network concept has several advantages: it is far from being complex and heavy, it is as fast as it is accurate and rather resistant. This will assist oncologists for which will also help in diagnosis. Finally, the implementation of the network will lead to the minimization of time and costs, which directly corresponds with the fact that there is no image management. Literally, no specific dataset was designed for this work; nevertheless, this study employed the Liver Tumor Segmentation dataset located online as a large pool of clinical images to train and evaluate the proposed FCN model. These data are used for the formation of a good and valid model since it presents different types of liver cancer. This work contributes to the ongoing body of literature on the employ of deep learning in medical image processing and offers implementable solutions for radiologists and physicians to enhance the degree of precision in liver segmentation. FCN model proposed in this work when trained on the Liver Tumor Segmentation dataset helps in offering efficient and effective solutions for advancing early diagnosis and hence the treatment of liver cancer.