rewrite the sentences in this Summary essay and arrangement the style of essay
Zarah Alyashi
Reoprt 2
Optical Biopsy using Spectroscopy Techniques and Artificial Intelligence for Cancer Diagnosis
In this lecture, the speaker mentioned two types of cancerous cell diagnosis first one by using biopsy with histopathological analysis. this method is the gold standard and it is oldest method for detect cancer cell but this technique has many disadvantages that making this way not effective in some advance levels of cancer because this type takes long time and weeks, this leads to loss of the time. Also, it is very invasive and subjective. then they developed technique using light and this is effective method for diagnose cancer called optical biopsy. in this method, they shine the light onto the sample and based on the signal that collect from the sample and they can define the signal between different other signals based on those matter interaction and different time. this method has features lie in taking short time it is rapid. furthermore, it can avoid subjectivity and non-invasive. Also, giving diagnostic information for early detection the cancerous tumors.
Optical biopsy method includes many different techniques such as fluorescence spectroscopy, Raman spectroscopy, SRS, CARS...etc. First, Resonance Raman Spectroscopy has two types: one that using (RRS) Resonance Raman spectroscopy, they couldn't detect some fingerprints of the molecules. So, using (NRRS) Non-Resonance Raman spectroscopy, they can analyze the tissue at the molecular level, also penetrate deep and measure large volume relatively. next, the speaker explained types of analytical method that depends on the data. For example, PCA uses algebra principle to find the components (PCs) of a data set and this is most strongly but these values could be negative. Then, the speaker listed other types analytical data like Non-negative Matrix Factorization (NMF) . Also, some of classifications such as Support Vector Machine (SVM) which dependent on data points that lie most closely to the decision boundary.