An analytical study on the detection of liver and liver tumors using supervised deep learning techniques
Nelaturi Nanda Prakash, V. G. Rajesh, Sk Hasane Ahammad, Ali Badr Roomi · AIP conference proceedings · 2024
This study looked at cancer incidence, patterns, trends, projections, and mortality, as well as the stage of the disease at the time of presentation and the type of treatment given to individuals diagnosed with liver cancer.It was possible to generate time trends in the cancer incidence rate.The approaches of Naib Bayes and SVM machine learning are described in this study for detecting liver cancer from pictures.This research was conducted to accomplish two goals: first, a training data set was used to train machine learning techniques; second, we constructed CNNs that achieved aided prognosis for liver cancers by categorizing them into seven classes.The adapted CNN, which associates unamplified pictures with medical information, produced end-to-end productivity to categorize malignant tumors.Initially, we built a CNN which relied on unamplified classifications to differentiate malignant from benign tumors.It has been detected that the result of the deep learning technique CNN outperformed with 98% accuracy.