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Liver Cancer Classification Using Single Pass Neural Networks Based on Ultrasound Images: A Review

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Abstract

Liver cancer is amongst the most cancer-related life threats worldwide. If not detected early, most liver diseases can lead to liver cancer. Early diagnosis of the disease depends on the physician's expertise; in some cases, it is difficult for even well-trained physicians to diagnose the condition by only visual inspection. With the help of automated detection systems (ADSs), medical experts can efficiently diagnose liver diseases, reducing the mortality rate due to liver cancer. This detailed study of various ADSs for liver disease detection is available today. The authors focus the analysis on the applications of single-pass neural networks in medical imaging. Also, special efforts explain the applications of single-pass neural networks in liver cancer classification using ultrasound imaging. Further, a detailed analysis is on the users' benefits of this non-iterative approach.

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  1. Dr. Babasheb Marathwada University, Aurangabad, Maharashtra, India

    Mahananda D. Malkauthekar & Ratnadeep R. Deshmukh

  2. Government College of Engineering, Aurangabad, India

    Avinash K. Gulve

  3. National Institute of Technology, Goa, India

    Damodar Reddy Edla

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Malkauthekar, M.D., Gulve, A.K., Deshmukh, R.R. et al. Liver Cancer Classification Using Single Pass Neural Networks Based on Ultrasound Images: A Review. Wireless Pers Commun 130, 241–268 (2023). https://doi.org/10.1007/s11277-023-10283-w

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