Summary In recent years, one of the largest causes of death in human beings is liver tumor and cancer. In the current scenario, identifying the cancer tumor manually is very difficult and takes a lot of time as the world is battling with COVID‐19. The doctors and physicians are busy in serving and curing them. To predict the stage of liver tumor and plan the treatment, the segmentation is done from CT scanned images. In this research, two‐stage automatic liver segmentation and tumor identification framework using a fully connected convolutional neural network (FC‐CNN) model is proposed. During the first stage, the liver region is segmented using level set method from the preprocessed (OTSU thresholding) CT images. The features are extracted and utilized to detect the liver tumor in the second stage. The developed FC‐CNN is trained and tested using the extracted second order statistical textural features to classify the tumor affected and normal image. The proposed FC‐CNN model is trained and tested with 3D‐IRCADb‐01 and Kaggle datasets. The tested results prove that the proposed FC‐CNN model outperforms other reported methods. From the performance analysis, it is observed that it achieves a good accuracy and sensitivity rate of 99.11% and 98.10%, respectively.