Deep Learning Approaches for Liver Tumor Segmentation

The method of automatically categorize and classify liver tumors of medical images using deep learning techniques is known as “liver tumor segmentation using CNN and LeNet.” This method feeds a neural network with the input images, and the network learns to identify relevant features and categorize the liver tumors. The method has two phases: categorization and division. The purpose of segmentation stage is to separate the image’s tumor from the surrounding healthy liver tissue. The liver tumors are separated from the computed tomography (CT) images using LeNet. The goal of the classification stage is to categorize the divided liver tumors. The segmented tumors’ uniqueness are learned by CNN, which then uses these features to classify. To increase the accuracy of segmentation, the CNN is trained using the segmented tumors. The Dice coefficient, which gauges overlap between the segmented tumor region with ground truth, metrics that can be used to assess the effectiveness of the liver tumor segmentation and classification with CNN and LeNet. The suggested approach has demonstrated encouraging outcomes in precise segmentation and classification of liver tumors, and it may help radiologists diagnose and treat liver cancer more accurately.