Glioma Tumor Grade Identification Using Artificial Intelligent Techniques.

Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medical applications especially for easy detection of tumor area, tumor type and grades. This paper presents automatic glioma tumor grade identification from magnetic resonant images using Wndchrm tool based classifier (Weighted Neighbour Distance using Compound Heirarchy of Algorithms Representing Morphology) and VGG-19 deep convolutional neural network (DNN). For experimentation, DICOM images are collected from reputed government hospital and the proposed intelligent system categorized the tumor into four grades such as low grade glioma, oligodendroglioma, anaplastic glioma and glioblastoma multiform. After preprocessing, features are extracted, optimized and then classified using Windchrm tool where the most significant features are selected on the basis of Fisher score. In the case of DNN classifier, data augmentation is also performed before applying the images into the deep learning network. The performance of the classifiers are analysed with various measures such as accuracy, precision, sensitivity, specificity and F1-score. The results showed reasonably good performance with a maximum classification accuracy of 92.86% for the Wndchrm classifier and 98.25% for VGG-19 DNN classifier. The results are also compared with similar recent works and the proposed system is found to have better performance.

Computer assisted recognition of breast cancer in biopsy images via fusion of nucleus-guided deep convolutional features.

BACKGROUND AND OBJECTIVE: Breast cancer is a commonly detected cancer among women, resulting in a high number of cancer-related mortality. Biopsy performed by pathologists is the final confirmation procedure for breast cancer diagnosis. Computer-aided diagnosis systems can support the pathologist for better diagnosis and also in reducing subjective errors. METHODS: In the automation of breast cancer analysis, feature extraction is a challenging task due to the structural diversity of the breast tissue images. Here, we propose a nucleus feature extraction methodology using a convolutional neural network (CNN), 'NucDeep', for automated breast cancer detection. Non-overlapping nuclei patches detected from the images enable the design of a low complexity CNN for feature extraction. A feature fusion approach with support vector machine classifier (FF + SVM) is used to classify breast tumor images based on the extracted CNN features. The feature fusion method transforms the local nuclei features into a compact image-level feature, thus improving the classifier performance. A patch class probability based decision scheme (NucDeep + SVM + PD) for image-level classification is also introduced in this work. RESULTS: The proposed framework is evaluated on the publicly available BreaKHis dataset by conducting 5 random trials with 70-30 train-test data split, achieving average image level recognition rate of 96.66  ±  0.77%, 100% specificity and 96.21% sensitivity. CONCLUSION: It was found that the proposed NucDeep + FF + SVM model outperforms several recent existing methods and reveals a comparable state of the art performance even with low training complexity. As an effective and inexpensive model, the classification of biopsy images for breast tumor diagnosis introduced in this research will thus help to develop a reliable support tool for pathologists.

Analysis of Carotid Plaque Using Multifractal method in Ultrasound Images.

This paper analyses the non-linear parameters of the plaque in carotid B-mode ultrasound images. In this work an attempt has been made to differentiate textural features of the symptomatic and asymptomatic plaque by multifractal method. The fractal dimension represents irregularity and hence the fractal nature. The interwoven sets of singularities are characterized by its own scaling behaviour, quantitatively represented as the fractal dimension, explains the multifractal behavior. The multifractal characteristics are plotted using a multifractal formalism proposed by Halsey. Certain multifractal features are extracted namely bandwidth (BW) of the spectrum and singularity exponent peak (SXPpeak). These extracted features have coefficient of variation in the range of 0.1 to 0.3; hence they have lower inter-subject variability. This analysis could aid in the study of the symptomatic and asymptomatic plaques.