Pancreatic Cancer Diagnosis from CT Scan Images Using Machine Learning Methods

This research focuses its objectives on the use of machine learning techniques in the detection of images of a CT scan to diagnose Pancreatic cancer. The work used an extensive database containing diverse CT images, more specifically, concerning pancreatic cancer patients. Two primary methodologies were implemented in this study. The first was the Support Vector Machine (SVM) classifier, one of the most used classifier methods in classification problems. The second component is the Convolution Neural Network (CNN), which has ResNet 50 at its base. The architecture of this model is commendably profound, and it can perceive the details of the images. The SVM classifier marked a precision of 83.07% for detecting cancer; such a level of accuracy can give clues, though it is still possible to define additional possibilities for improving the classification of complicated cases. On the other hand, the CNN model based on the ResNet50 learned a more accurate model than the training accuracy of 90. 01%. Such a performance increase is explained quite well by deep learning methodologies’ capabilities, especially in medical image analysis. These studies draw attention to the possibility of utilizing more sophisticated CNN models to promote the accuracy and reliability of pancreatic cancer diagnosis. The increased accuracy also has ramifications for the diagnostic phase of pancreatic cancer, but also it emphasizes the necessity of using complex algorithms for early identification of this disease.