Research on Medical Image Classification Based on Machine Learning

Project Code :TCPGPY371

Objective

In this paper, we propose a new method for CT pathological image analysis of brain and chest to extract image features and classify images. A semi supervised learning based image classification method is proposed using Deep Neural Network.

Abstract

With the increasing demand for faster and more accurate treatment, medical imaging plays an increasingly important role in the early detection, diagnosis and treatment of diseases. Thanks to the development of physics, electronic engineering and computer science and technology, the resolution of medical image is higher and higher, and the image mode is more and more abundant.

At the same time, the number of medical image is increasing rapidly. At present, X-ray imaging, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET / CT), ultrasound imaging are widely used in clinic.The key to achieve accurate diagnosis and treatment is the accurate interpretation of medical images, but the interpretation of images highly depends on the subjective judgment of doctors, so doctors at different levels have great deviation on the results of image interpretation.

Keywords: Generative Adversarial Network; Deep Learning; Feature Extraction; Image Classification

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.