Liver Cancer Detection and Classification Using Raspberry Pi

Abstract

In practical radiology, early diagnosis and precise categorization of liver cancer are difficult issues. Manual segmentation is also a time-consuming process. So, utilizing various methodologies based on an embedded system, we detect liver cancer from abdominal CT images using automated liver cancer segmentation and classification. The objective is to categorize CT scan images of primary and secondary liver disease using a Back Propagation Neural Network (BPNN) classifier, which has greater accuracy than previous approaches. In this work, a newly proposed method is shown which has four phases: image preprocessing, image segmentation, extraction of the features, and classification of the liver. Level set segmentation for segmenting the liver from abdominal CT images and Practical Swarm Optimization (PSO) for the tumor segmentation. Then the features from the liver are extracted and given to the BPNN classifier to classify the liver cancer. These algorithms are implemented on the Raspberry Pi. Then it serially interfaces with the MAX3232 protocol via serial communication. The GSM 800C module is connected to the system to send SMS as primary or secondary cancer. The BPNN classification technique achieved an excellent accuracy of 97.98%. The experimental results demonstrate the efficiency of this proposed approach, which provides excellent accuracy with good results.