A Artificial Intellegent algorithms for Tumor Disease Detection: systematic Literature Review - Muhammad Hafid Alkarim; Iswanto Suwarno; Muhammad Ahmad Baballe; Mukhtar Ibrahim Bello (Prodi Fisika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta, 2023)
A Tumor is a swelling in the body caused by cells that multiply abnormally. Tumors or neoplasms consisting of benign tumors and malignant tumors. Benign tumors can grow larger but do not spread to other body tissues. Malignant tumors are cancers that attack the entire body and are uncontrollable. Comparison between the cell nucleus with the cytoplasm of malignant tumors, while benign tumors are the same as normal cells. Cancer cells can develop rapidly. These cells attack and damage body tissues through the bloodstream and lymph vessels so that they can grow in new places. One way to detect tumor disease is by utilizing Artificial intelligence algorithms for tumor Disease Detection. The purpose of this paper is for the development of Artificial Intellegent algorithms for the detection of tumor Diseases and optimization of Artificial Intellegent algorithms for the detection of tumor Diseases. This research uses systematic literature review by using preferred Reporting Items for Systematic Review (PRISMA). The results of screening and selection of articles obtained 64 potential articles that have met the inclusion criteria. The results showed that with earlier detection, a person can check tumor disease earlier using the help of Artificial intelligence algorithms. The results of research on the development of Artificial intelligence algorithms for detection of tumor Diseases have found Artificial intelligence algorithms that can be used to reduce the risk of tumor disease. Optimization of Artificial Intelegency algorithms for tumor classification, performing new data processing methods such as artificial intelligence can be selected to provide the accuracy of classification and diagnosis, exploration of detection limits is a very important aspect in tumor diagnosis based on SERS, finding improved and suitable nanoparticle substrates so as to significantly improve the original Raman signal.