Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images

Background To reduce the intensity of the work of doctors, pre-classification work needs to be issued. In this paper, a novel and related liver microscopic image classification analysis method is proposed. Objective For quantitative analysis, segmentation is carried out to extract the quantitative information of special organisms in the image for further diagnosis, lesion localization, learning and treating anatomical abnormalities and computer-guided surgery. Methods in the current work, entropy based features of microscopic fibrosis mice’ liver images were analyzed using fuzzy c-cluster, k-means and watershed algorithms based on distance transformations and gradient. A morphological segmentation based on a local threshold was deployed to determine the fibrosis areas of images. Results the segmented target region using the proposed method achieved high effective microscopy fibrosis images segmenting of mice liver in terms of the running time, dice ratio and precision. The image classification experiments were conducted using Gray Level Co-occurrence Matrix (GLCM). The best classification model derived from the established characteristics was GLCM which performed the highest accuracy of classification using a developed Support Vector Machine (SVM). The training model using 11 features was found to be as accurate when only trained by 8 GLCMs. Conclusion The research illustrated the proposed method is a new feasible research approach for microscopy mice liver image segmentation and classification using intelligent image analysis techniques. It is also reported that the average computational time of the proposed approach was only 2.335 seconds, which outperformed other segmentation algorithms with 0.8125 dice ratio and 0.5253 precision

Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network

Hepatic granuloma develops in the early stage of liver cirrhosis which can seriously injury liver health. At present, the assessment of medical microscopic images is necessary for various diseases and the exploiting of artificial intelligence technology to assist pathology doctors in pre-diagnosis is the trend of future medical development. In this article, we try to classify mice liver microscopic images of normal, granuloma-fibrosis 1 and granuloma-fibrosis2, using convolutional neural networks (CNNs) and two conventional machine learning methods: support vector machine (SVM) and random forest (RF). On account of the included small dataset of 30 mice liver microscopic images, the proposed work included a preprocessing stage to deal with the problem of insufficient image number, which included the cropping of the original microscopic images to small patches, and the disorderly recombination after cropping and labeling the cropped patches In addition, recognizable texture features are extracted and selected using gray the level co-occurrence matrix (GLCM), local binary pattern (LBP) and Pearson correlation coefficient (PCC), respectively. The results established a classification accuracy of 82.78% of the proposed CNN based classifiers to classify 3 types of images. In addition, the confusion matrix figures out that the accuracy of the classification results using the proposed CNNs based classifiers for the normal class, granuloma-fibrosisl, and granuloma-fibrosis2 were 92.5%, 76.67%, and 79.17%, respectively. The comparative study of the proposed CNN based classifier and the SVM and RF proved the superiority of the CNNs showing its promising performance for clinical cases

A Comparative Analysis of Machine Learning Models for Banking News Extraction by Multiclass Classification With Imbalanced Datasets of Financial News: Challenges and Solutions

Online portals provide an enormous amount of news articles every day. Over the years, numerous studies have concluded that news events have a significant impact on forecasting and interpreting the movement of stock prices. The creation of a framework for storing news-articles and collecting information for specific domains is an important and untested problem for the Indian stock market. When online news portals produce financial news articles about many subjects simultaneously, finding news articles that are important to the specific domain is nontrivial. A critical component of the aforementioned system should, therefore, include one module for extracting and storing news articles, and another module for classifying these text documents into a specific domain(s). In the current study, we have performed extensive experiments to classify the financial news articles into the predefined four classes Banking, Non-Banking, Governmental, and Global. The idea of multi-class classification was to extract the Banking news and its most correlated news articles from the pool of financial news articles scraped from various web news portals. The news articles divided into the mentioned classes were imbalanced. Imbalance data is a big difficulty with most classifier learning algorithms. However, as recent works suggest, class imbalances are not in themselves a problem, and degradation in performance is often correlated with certain variables relevant to data distribution, such as the existence in noisy and ambiguous instances in the adjacent class boundaries. A variety of solutions to addressing data imbalances have been proposed recently, over-sampling, down-sampling, and ensemble approach. We have presented the various challenges that occur with data imbalances in multiclass classification and solutions in dealing with these challenges. The paper has also shown a comparison of the performances of various machine learning models with imbalanced data and data balances using sampling and ensemble techniques. From the result, it’s clear that the performance of Random Forest classifier with data balances using the over-sampling technique SMOTE is best in terms of precision, recall, F-1, and accuracy. From the ensemble classifiers, the Balanced Bagging classifier has shown similar results as of the Random Forest classifier with SMOTE. Random forest classifier's accuracy, however, was 100% and it was 99% with the Balanced Bagging classifier