Search

A recent study conducted by researchers at Tzu Chi University in Taiwan aimed to predict the risk factors of disease recurrence in patients with advanced-stage head and neck cancer. The study utilized a machine learning algorithm called eXtreme Gradient Boosting (XGBoost) to analyze clinical data from 187 patients who received surgery and adjuvant radiotherapy with or without chemotherapy. The researchers found that pathological lymph node status and whether the patient was receiving chemotherapy were the most important factors associated with treatment outcome. The study concluded that further research is needed to develop a stronger model for predicting outcomes in these patients. [Extracted from the article]

Researchers from the University of Anbar have developed a deep learning algorithm using EfficientNet B3 for the detection of lung cancer. The algorithm aims to improve detection accuracy and potentially revolutionize medical imaging and patient care. The model was able to classify four different types of lung cancer with an accuracy rate of 96%, showing a 2.13% improvement compared to the best-trained classifier. The researchers believe that this model has the potential to enhance lung cancer detection and support early diagnosis. [Extracted from the article]