Automatic Detection of Liver Cancer Using Artificial Intelligence and Imaging Techniques—A Review

Naaqvi, Zunaira; Akbar, Shahzad; Hassan, Syed Ale; Khalid, Aima; Bashir, Muhammad Jawad

doi:10.1007/978-981-19-2057-8_12

Cited by 2 publications

(1 citation statement)

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“…Risk factors for liver cancer comprise smoking, heavy drinking, having a family history of liver cancer, being overweight or diabetic, having the hepatitis B or C virus, and having a weakened immune system. Symptoms of liver cancer include stomach pain, loss of weight, jaundice, belly swelling, nausea, weariness, fever, back pain, vomiting, or itching [10,11,12,13]. Other symptoms include jaundice and itching.…”

Section: Introductionmentioning

confidence: 99%

Automatic Liver Cancer Detection Using Deep Convolution Neural Network

Napte,

Mahajan,

Urooj

2023

IEEE Access

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Automatic liver cancer detection (ALCD) is very crucial in automatic biomedical image analysis diagnosis as it is the largest organ in the body and plays a significant role in the metabolic process as well as the elimination of toxins. In the last decade, various machine and deep learning schemes have been investigated for automatic ALCD using computed tomography (CT) images. However, ALCD in CT images is challenging because of the noise, intricate structure of abdominal computed tomography (CT) images, and textural changes throughout the CT images making liver segmentation a vital challenge that may result in both under-segmentation (u-seg) and over-segmentation (o-seg) of the organ. This paper presents liver segmentation based on the proposed Edge Strengthening Parallel UNet (ESP-UNet) for liver segmentation to avoid the u-seg and o-seg of the liver in CT images. Further, it offered ALCD based on lightweight sequential Deep Convolution Neural Networks (DCNN). The consequences of ESP-UNet DCNN-based ALCD are evaluated based on accuracy, recall, precision, and F1-score. The suggested approach provides a noteworthy improvement in ALCD over the traditional state of arts.

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Section: Introductionmentioning

confidence: 99%