Liver cancer is a life-threatening disease, with an estimated deaths of 2 million per year
worldwide. Due to availability of abundant computational power recently the deep learning …

The high variation in the size of tumor, location, depth, intimate boundaries of other organs
around liver, liver tumor segmentation is the most important yet challenging task. The size …

Every year, liver cancer claims the lives of more than 1.5 million people around the world.
Liver cancer can be detected early with Computed Tomography (CT). This could save …

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.” …

Background: Liver cancer is the sixth most common cancer worldwide. According to WHO
data in 2017, the liver cancer death in Ethiopia reached 1040 (0.16%) from all cancer …

The last several decades have seen a lot of activity in the medical image processing domain
especially when it comes to the segmentation of liver and liver tumours. The survival rate of …

Liver tumors are one of the life-threatening cancers with the fastest-growth rates worldwide.
Early detection of tumors may therefore reduce morbidity and increase the survival rate. The …

In recent years, with the rapid advancements in deep learning, image processing has
progressively attracted the attention of medical researchers to achieve accurate diagnoses …

Accurate segmentation and categorization of liver tumours are crucial for the diagnosis and
management of carcinoma or metastases. The liver tumour presents a challenging problem …

One of the biggest causes of death among humans worldwide is liver disease. Currently,
manually identifying the cancer tissue is a difficult and laborious task. By segmenting liver …