Research on Medical Image Classification Based on Machine Learning

Abstract

In this paper, we propose a new method for CT pathological image analysis of brain and chest to extract image features and classify images. Because the deep neural network needs a large number of labeled samples to complete the training, and the cost of medical image labeling is very high, the training samples needed to train the deep neural network are insufficient. In this paper, a semi supervised learning based image classification method is proposed, which uses a small amount of labeled pathological image data to train the network model, and then integrates the features extracted by the network to classify the image. The results show that the classification effect of the neural network is better than convolution neural network and other traditional image classification models. To some extent, it can reduce the dependence of neural network on a large number of training samples, and effectively reduce the over fitting phenomenon of the network. Through the analysis of the overall classification accuracy and kappa coefficient of different classification methods under different sample numbers, it is found that the overall classification accuracy and kappa coefficient are increasing with the increasing number of training samples. Especially in the case of a small number of training samples, compared with other deep neural networks and traditional classification methods, the classification accuracy of the counter neural network is about 10% higher than that of other neural networks and traditional classification methods, and the advantages are more obvious. INDEX TERMS Generative adversarial network, deep learning, feature extraction, image classification.

Viral and Bacterial Pneumonia Diagnosis via Deep Learning Techniques and Model Explainability

“…The diagnosis process performs with Machine Learning or Deep Learning can help physicians investigate the medical images conveniently and reduce the analysis time. Several studies have resolved the challenging tasks such as medical image classification [5], [6], skin cancer detection using images [7], or 3D image biomedical segmentation [8].…”

“…The implementations of Machine Learning or Deep Learning in health care are influenced by the accuracy of medical data. Specifically, the annotation progress in the medical image is based on medical professional knowledge, medical industry standard, and medical system [6].…”

Explanation of the Convolutional Neural Network Classifying Chest X-Ray Images Supporting Pneumonia Diagnosis

“…Besides, it can deal with the imbalanced data problem in the hyperspectral image. The authors in [13] proposed semi-supervised learning for CT pathological images analysis of the brain and chest. The generated antagonism network was trained with a small amount of labelled data and combined the extracted features for classifying.…”

“…Several deep learning approaches in medical imaging have been proposed for disease detection and diagnosis, skin cancer classification [11], or a deep encoder-decoder architecture for 3D image biomedical segmentation [12]. However, the annotation progress in the medical image is based on medical professional knowledge, medical industry standard, and medical system [13]. Thus, it requires a large human and material resources.…”

Spectral-Spatial Active Learning With Structure Density for Hyperspectral Classification

“…The focus of AL is that try to conquer the labeling choke point by asking queries in the form of unlabeled samples to be annotated by an oracle [33]. In particular, AL is successfully applied to deal with classification problem in the HSI applications as a new machine learning method [34], [35], its main goal is to effectively find high-information samples in the unlabeled sample dataset and then retrain the pixelwise classifier efficiently by iteratively expanding the labeled samples with an iterative manual labeling processing. By choosing unlabeled samples in a smart active query strategy, the amount of initial labeled samples (training samples) required for training a robust classifier can be observably reduced, consequently, lessening the labeling costs and time [36].…”