DESIGN OF A CNN-BASED EXTRACTOR FOR OPTIMAL CLASSIFICATION OF PANCREATIC CANCER

Subject Area: COMPUTER SCIENCE

This study presents a Convolutional Neural Network (CNN) based extractor for the automated tracking and detection of pancreatic tumors using a combination of CNN, as a feature extractor, ResNet and Region Proposal Networks (RPN) for classification tumor localization in CT images. The proposed system aims to enhance diagnostic accuracy and assist radiologists in identifying early-stage tumors. The methodology used is Agile. The total sample size of data collected for the study is 2057 CT images of PC from Nnamdi Azikiwe University Teaching Hospital (NAUTH), Awka, Anambara state. Proposed CNNs with multi scale convolutional process was applied for to extract relevant features from CT scan images. The extracted features were applied to train ResNet as the classifier, while RPN is then used to detect potential regions of interest where tumors might be present. The combined model is evaluated on a pancreatic cancer dataset, where various metrics such as F1-score, precision, recall, and confusion matrices are used to assess its performance. The model achieved an F1-score of 0.97 with a confidence threshold of 0.395 and a recall of 1.0 at the same threshold. The confusion matrix indicates a 93% correct prediction for tumors, with the rest categorized under background and no-tumor classes. The mAP score, a key metric for object detection models, improved significantly from 0.1973 to 0.85331, demonstrating the model's robustness in identifying tumors. These metrics suggest a highly accurate model capable of distinguishing between tumor and non-tumor regions, minimizing false positives and false negatives.